Job Search

GCS is acting as an Employment Agency in relation to this vacancy.

Read less

Business Analyst | Digital Transformation & Process Improvement

Location: Ghent, Belgium

Are you passionate about business process improvement, digital transformation, and helping organisations embrace new technologies? We are looking for a Business Analyst to play a key role in driving digital innovation across a growing professional services organisation.

This is an opportunity to work at the intersection of business, technology, and change management, helping teams optimise processes, adopt new digital tools, and create better experiences for both employees and clients.

The Role

As a Business Analyst, you will support the digitalisation of business processes and customer journeys across multiple offices. Working closely with stakeholders, you will analyse existing ways of working, identify opportunities for improvement, and implement solutions that drive efficiency and value.

You will be responsible for the full lifecycle of process optimisation, from analysis and design through to implementation and continuous improvement.

Key Responsibilities

Business Analysis

Analyse current business processes and identify opportunities for improvement.Define future-state processes and recommend appropriate digital solutions.Drive process simplification, standardisation, automation, and optimisation initiatives.Translate business requirements into practical and scalable solutions.

Change Management

Develop and support change and implementation strategies for business-critical initiatives.Work closely with key users and operational teams to drive successful adoption of new processes and technologies.Act as a trusted advisor and partner for continuous improvement initiatives.

Digital Transformation & Advisory

Advise management and business teams on process design and digital transformation strategies.Monitor emerging technologies, including AI, machine learning, automation, and digital workplace tools.Identify opportunities to leverage new technologies and platform capabilities.Facilitate knowledge-sharing sessions and promote best practices across the organisation.

What We're Looking For

Several years of experience as a Business Analyst, Process Analyst, Digital Transformation Consultant, or in a similar role.Experience within the accountancy, finance, or professional services sector is highly desirable.Strong understanding of business processes and digital transformation initiatives.Experience working with digital platforms and process improvement projects.Fluent in Dutch and English. French would be a strong advantage.Excellent communication and stakeholder management skills.Strong analytical and problem-solving abilities.A collaborative, hands-on approach and the ability to influence change across the business.Comfortable translating complex concepts into practical solutions for end users.Willingness to travel between offices and work closely with stakeholders on-site.

What's On Offer?

A full-time position within a growing and ambitious organisation.The opportunity to shape and influence major digital transformation initiatives.Exposure to innovative technologies, including AI and automation.A collaborative and forward-thinking working environment.Excellent opportunities for professional development and career progression.The chance to make a tangible impact on how the business operates and evolves.

If you're passionate about digital transformation, process improvement, and helping businesses embrace the future of work, we'd love to hear from you.

GCS is acting as an Employment Agency in relation to this vacancy.

Read less

GCS is acting as an Employment Business in relation to this vacancy.

Read less

Senior Engineering Manager (Chapter Lead) - AI Products Freelance engagement · Ghent, Belgium Location: Ghent, Belgium (three office days per week; visits to any of the group's offices count as office days). Regular time in the Netherlands. Engagement: Freelance, long-term Reports to: Head of Product & AI Rate: Competitive - to be discussed About the client Our client is an accountancy and advisory group operating across multiple countries in Western Europe, with several thousand colleagues. Firms that join the group keep their own name, their people and their client relationships. What the group adds is scale: shared platforms, shared data and shared AI products. The role The group builds AI products for accounting work - agents that prepare and review the work, and the surfaces accountants and their clients use them in. The products are built in squads, each run by a Product Manager. You build the engineering team and are the line manager of every engineer in those squads. Internally the role is called Chapter Lead Engineering. Most of today's engineering is done by specialist partners. You lead the transition to an internal team, squad by squad, and decide when each one is ready. You start with the first hires and build a chapter of ten to fifteen engineers across Belgium and the Netherlands by the end of 2027. You are the engineers' hierarchical manager: hiring, development and performance. You do not lead their day-to-day work. That happens inside the squad, where the Product Manager sets the priorities, because nobody can run the daily work of nine squads. Architecture is owned by the AI Architect; how the team engineers, reviews, tests and ships is yours. You are not a member of a squad and you carry no squad's delivery commitments, but you stay close to the code. This is a new position in a department that is still being built, so part of the way of working is yours to define. What you'll own The team. Recruitment, onboarding, staffing across the squads, and keeping the people you hired. The engineering career ladder and how the group hires against it, agreed with HR. The craft. Engineering standards, code review, testing and release practice, how agents are evaluated before they ship (with the analysts), how engineers use AI coding tools, and the quality of what ships. The dev stack. Code hosting and CI/CD, environments, developer tooling, licences and access. Everyone who builds at the group uses the same set, and you decide what is in it. How production is run. The on-call model, incident practice and run-and-maintain staffing for agents in production. The Product Manager decides what risk a product carries. You decide how the team responds when something breaks. The insourcing transitions. Which squad moves from partner to internal engineering next, what has to be true before it does, and the handover itself. The engineering organisation is yours: who gets hired, how the team builds, and when a squad is ready to run without a partner. What you need One thing matters more than the rest: you have led engineers who shipped AI products to production, and you stayed responsible for what happened after launch. Eight or more years in software engineering, three or more of them managing engineers Conversational Dutch or French, and fluent English A plus: You have built an engineering team from the first hires to ten or more You have led a transition from partner-built to in-house engineering This role suits you if You have hired engineers yourself and lived with the result You can read a pull request and tell whether the team is in trouble You would rather build a team than run one you inherited You can hold a standard across squads that all want an exception A function that is still taking shape appeals to you Confidential - client name available on discussion. For more information contact Kashif Mahmood, GCS · 07704 086 433 · [email protected]

GCS is acting as an Employment Business in relation to this vacancy.

Read less

Senior Product Manager - AI Products (Accounting & Tax) Ghent, Belgium · Freelance (Belgium only) or permanent full-time · Reports to Head of Product & AI About the client Our client is an established accountancy and advisory group operating across several European markets, with thousands of colleagues and a large portfolio of local office brands. Firms that join the group keep their own name, their people and their client relationships. What the group adds is scale: shared platforms, shared data and shared AI products. The role The group builds AI products for accounting work. You will own the agents that prepare and review the work, and the surface the accountants use them in. This is a newly created position in a department that is still being built, so part of the way of working is yours to define. This is an individual-contributor role. What you'll own bullet The roadmap for your domain. You decide what your domain team works on next and why: the engineers, your business analyst, and the partner engineers building in your domain. bullet The release decision. You decide whether something ships, where a person stays in the loop, and what risk the business is prepared to carry on a client's filing. bullet The product's budget. You manage it, including the scope of the work placed with specialist build partners. bullet The domain, end to end. Discovery with the accountants, the order in which things get built, and what you stop doing. The roadmap for your domain is yours end to end, and the partner scope runs through you. What you need One thing matters more than the rest: you have owned a product that real users used after launch, and you stayed responsible for it. bullet Five or more years of professional experience. bullet Hands-on experience building with AI, beyond using a chatbot. You have made something that works and that someone other than you has used. bullet Dutch or French, plus English. Three routes into this role bullet From product. You have run a product or a product line and want a domain with real subject matter behind it. bullet From the practice. You are an accountant, tax specialist or auditor who moved into product or digital work, and you have built things rather than only asked for them. bullet From consulting. You have run this kind of programme for clients and would rather own the result than hand it over. This role suits you if You would rather see the work in an office than read about it in a ticket. bullet bullet You are comfortable deciding that a feature is good enough to ship. bullet You want a whole domain rather than a feature at a time. bullet You have patience with people who have done the work the same way for twenty years. bullet You can explain a trade-off to an accountant and an accounting rule to an engineer. bullet A function that is still taking shape appeals to you. What's on offer bullet Competitive freelance day rate, or a permanent package - both benchmarked to experience and discussed openly at first conversation. bullet A long-term engagement in a function being built from scratch. bullet Three office days a week in Ghent. Visits to any of the group's other offices in Belgium count as office days. bullet Regular time in the offices, with the accountants who use what you build. bullet A direct reporting line to the Head of Product & AI. Next steps This role is being handled exclusively by GCS. To apply, or for a confidential conversation about the client and the wider function, contact: Kashif Mahmood - GCS

GCS is acting as an Employment Business in relation to this vacancy.

Read less

MLOps EngineerRole Overview

We are seeking an experienced MLOps Engineer to design, build, and operate the platforms, infrastructure, and processes required to deploy and manage machine learning and AI solutions at scale.

The MLOps Engineer will bridge the gap between data science, AI engineering, software engineering, data engineering, and cloud/platform teams, ensuring that models can be developed, tested, deployed, monitored, and maintained reliably in production.

The ideal candidate will have strong experience across cloud infrastructure, automation, CI/CD, machine learning lifecycle management, model deployment, monitoring, and DevOps practices.

Key ResponsibilitiesDesign and implement scalable MLOps platforms and architectures for machine learning and AI workloads.Build automated pipelines covering the full ML lifecycle, from data and model development through to production deployment and monitoring.Develop and maintain CI/CD and continuous training (CT) pipelines for machine learning models.Automate model testing, validation, deployment, rollback, and lifecycle management.Implement model versioning, experiment tracking, model registries, and reproducible ML workflows.Build and maintain infrastructure for model training, inference, and serving.Deploy machine learning models across cloud, containerised, and Kubernetes-based environments.Implement automated monitoring for model performance, data quality, drift, availability, and operational health.Establish processes for model retraining and continuous improvement.Work closely with data scientists and AI engineers to productionise models and AI applications.Collaborate with data engineers to integrate ML pipelines with enterprise data platforms.Implement infrastructure and environments using Infrastructure as Code (IaC).Develop reusable tooling, frameworks, templates, and deployment patterns for ML teams.Optimise compute, storage, model serving, and cloud infrastructure costs.Implement appropriate security, access control, secrets management, and compliance controls.Support deployment of both traditional machine learning models and Generative AI/LLM applications.Establish observability and operational support processes for production AI/ML systems.Troubleshoot complex infrastructure, deployment, pipeline, model-serving, and performance issues.Define and document MLOps standards, architecture patterns, engineering practices, and operational procedures.Mentor data scientists, AI engineers, and software engineers on production ML practices.Required Skills and ExperienceStrong experience in MLOps, DevOps, machine learning engineering, cloud engineering, or related disciplines.Strong understanding of the end-to-end machine learning lifecycle.Strong programming and scripting skills, particularly Python.Experience building and managing CI/CD pipelines.Experience with containerisation technologies such as Docker.Experience with Kubernetes and container orchestration.Strong experience with at least one major cloud platform such as Azure, AWS, or Google Cloud Platform.Experience with Infrastructure as Code tools such as Terraform.Experience deploying and managing machine learning models in production.Experience with model versioning, experiment tracking, and model registries.Experience with ML platforms and tools such as MLflow, Kubeflow, Azure Machine Learning, AWS SageMaker, or equivalent.Strong understanding of Git, automated testing, deployment automation, and DevOps practices.Experience implementing monitoring, logging, observability, and alerting.Understanding of data pipelines, data quality, model performance, and data/model drift.Strong understanding of cloud security and identity/access management.Excellent troubleshooting and problem-solving skills.Desirable SkillsExperience supporting Generative AI and LLM workloads.Experience deploying RAG applications and vector search infrastructure.Experience with LLM evaluation, monitoring, and observability.Experience with platforms such as Databricks, Snowflake, Azure OpenAI, Amazon Bedrock, or Google Vertex AI.Experience with Apache Spark and distributed data processing.Experience with Kafka or other event-streaming technologies.Experience with GitHub Actions, GitLab CI/CD, Azure DevOps, Jenkins, or equivalent.Experience with Kubernetes tools such as Helm.Experience with cloud-native monitoring technologies.Experience implementing automated model retraining pipelines.Knowledge of responsible AI, AI governance, security, and regulatory requirements.Experience with FinOps and optimisation of cloud-based ML workloads.MLOps Platform Responsibilities

The MLOps Engineer will typically be responsible for establishing and maintaining capabilities across:

Source Control - Git-based development and version management.CI/CD - Automated build, test, validation, and deployment pipelines.Experiment Tracking - Tracking experiments, parameters, metrics, and artefacts.Model Registry - Model versioning, approval, promotion, and lifecycle management.Model Serving - Reliable and scalable online and batch inference.Infrastructure - Automated provisioning of ML environments and compute.Monitoring - Model, application, infrastructure, and data monitoring.Data & Model Drift - Detection and remediation of changes affecting model performance.Security - Identity, access control, secrets, network security, and compliance.Governance - Auditability, lineage, approvals, and model lifecycle controls.Automation - Reducing manual intervention across the ML lifecycle.Key CompetenciesMLOps & ML Lifecycle ManagementCloud EngineeringDevOps & CI/CDPython & AutomationDocker & KubernetesInfrastructure as CodeModel Deployment & ServingMLflow / ML PlatformsMonitoring & ObservabilityModel & Data DriftCloud SecurityGenerative AI & LLM OperationsPerformance & Cost OptimisationTechnical Problem SolvingTypical Experience Level

4-9+ years of experience across MLOps, DevOps, cloud engineering, machine learning engineering, or related disciplines, with demonstrable experience deploying and operating machine learning or AI solutions in production.

A strong candidate should be capable of taking an ML/AI solution from development to production, establishing the automation, infrastructure, monitoring, governance, and operational processes required to run it reliably at scale.

GCS is acting as an Employment Business in relation to this vacancy.

Read less

AI EngineerRole Overview

We are seeking an experienced AI Engineer to design, develop, deploy, and maintain AI and machine learning solutions that deliver measurable business value. The ideal candidate will combine strong software engineering skills with practical experience in machine learning, generative AI, data, and cloud technologies.

The AI Engineer will work closely with data scientists, data engineers, architects, software engineers, and business stakeholders to translate business requirements into scalable, reliable, and production-ready AI solutions.

Key ResponsibilitiesDesign, develop, and deploy AI and machine learning solutions for business and operational use cases.Develop production-grade AI applications using Python and modern AI/ML frameworks.Build and integrate machine learning models into enterprise applications and data platforms.Develop solutions using Generative AI, Large Language Models (LLMs), Natural Language Processing (NLP), and Retrieval-Augmented Generation (RAG) where appropriate.Design and implement AI-powered applications, services, APIs, and automation workflows.Integrate foundation models and AI services into enterprise technology environments.Develop and optimise prompt engineering, model orchestration, and evaluation approaches.Design and implement RAG architectures using enterprise data, vector databases, embeddings, and knowledge bases.Build data preparation, feature engineering, model training, and inference pipelines.Evaluate models and AI solutions for accuracy, reliability, performance, cost, and business effectiveness.Implement monitoring, observability, testing, and continuous improvement for AI systems.Apply responsible AI principles covering security, privacy, bias, explainability, and appropriate use of AI.Work with data engineering teams to ensure AI solutions have reliable and appropriately governed data.Collaborate with cloud and platform engineering teams to deploy scalable AI workloads.Implement CI/CD and MLOps practices to support repeatable and reliable model deployment.Troubleshoot model, application, data, performance, and integration issues.Keep up to date with developments in AI, machine

GCS is acting as an Employment Business in relation to this vacancy.

Read less

Databricks Data LeadRole Overview

We are seeking an experienced Databricks Data Lead to provide technical leadership in the design, development, and delivery of modern cloud-based data platforms using Databricks. The role will be responsible for establishing data engineering standards, leading technical delivery, and ensuring that data solutions are scalable, secure, reliable, and aligned with business objectives.

The Databricks Data Lead will work closely with data architects, data engineers, analysts, data scientists, business stakeholders, and cloud engineering teams to deliver enterprise data and analytics capabilities.

Key ResponsibilitiesLead the design and implementation of scalable enterprise data platforms using Databricks.Provide technical leadership across data engineering, data integration, data transformation, and analytics initiatives.Define and implement modern data architecture patterns using the Databricks Lakehouse architecture.Design and implement data pipelines using Apache Spark, PySpark, SQL, and Databricks.Establish standards and best practices for data ingestion, transformation, processing, storage, and consumption.Lead the development of batch and real-time/streaming data pipelines.Design and implement data lake and lakehouse solutions using technologies such as Delta Lake.Define approaches for data modelling, data partitioning, optimisation, and storage management.Lead the implementation of data governance, security, access control, data quality, lineage, and metadata management.Work with architects and engineering teams to establish appropriate integration patterns between Databricks and enterprise data sources and platforms.Drive performance optimisation and cost management across Databricks workloads.Establish development standards covering coding, testing, deployment, monitoring, and operational support.Implement and promote CI/CD and DevOps practices for data engineering solutions.Support Infrastructure as Code and automated deployment approaches where appropriate.Lead technical design reviews and provide guidance on complex data engineering challenges.Mentor and support data engineers, helping establish consistent engineering practices and technical standards.Work with delivery managers and product owners to translate business requirements into technical solutions.Estimate technical effort, identify dependencies and risks, and contribute to delivery planning.Troubleshoot and resolve complex data pipeline, performance, reliability, and integration issues.Produce and maintain technical documentation, architecture diagrams, design specifications, and engineering standards.Evaluate new Databricks capabilities and emerging data technologies and recommend opportunities for adoption.Required Skills and ExperienceSignificant experience in data engineering, data architecture, or cloud data platforms.Strong hands-on experience with Databricks and the Lakehouse architecture.Strong knowledge of Apache Spark, PySpark, and SQL.Experience designing and implementing enterprise-scale data pipelines.Strong understanding of Delta Lake and modern data lake/lakehouse architectures.Experience working with cloud platforms such as Azure, AWS, or Google Cloud Platform.Strong understanding of data warehousing, data lakes, data modelling, and data integration.Experience developing both batch and streaming data solutions.Strong understanding of data quality, governance, security, and access-control principles.Experience with source control, CI/CD, automated testing, and DevOps practices.Experience with data orchestration technologies such as Databricks Workflows, Apache Airflow, Azure Data Factory, or equivalent.Experience integrating data from relational databases, APIs, files, event streams, and enterprise applications.Strong programming experience in Python and/or Scala.Experience leading technical teams or providing technical leadership to data engineering teams.Strong problem-solving, communication, and stakeholder-management skills.Desirable SkillsExperience with Unity Catalog and Databricks governance capabilities.Experience with Delta Live Tables / Lakeflow Declarative Pipelines.Experience with Databricks SQL and SQL Warehouses.Experience with Structured Streaming and technologies such as Kafka.Experience with dbt or other modern data transformation frameworks.Experience with Terraform or other Infrastructure as Code technologies.Experience implementing data mesh or domain-oriented data architectures.Experience with data cataloguing, lineage, and data governance platforms.Experience with cloud-native services across Azure, AWS, or GCP.Experience with machine learning, AI, or advanced analytics platforms.Databricks or relevant cloud certifications.Leadership ResponsibilitiesSet technical direction and engineering standards for the data platform.Lead and mentor data engineers and provide technical guidance across delivery teams.Review technical designs and ensure alignment with enterprise architecture principles.Identify opportunities to improve platform scalability, reliability, performance, and cost efficiency.Promote engineering best practices, automation, reusable components, and standardised patterns.Act as a technical point of contact between engineering teams, architecture teams, and business stakeholders.Support recruitment, technical assessments, capability development, and knowledge sharing where required.Key CompetenciesDatabricks & Lakehouse ArchitectureApache Spark & PySparkData EngineeringData Architecture & ModellingDelta LakeCloud Data PlatformsData Governance & SecurityData Pipeline DesignPerformance & Cost OptimisationCI/CD & DevOpsTechnical LeadershipStakeholder ManagementMentoring & Team DevelopmentTypical Experience Level

7-12+ years of experience in data engineering, data architecture, analytics engineering, or related disciplines, including significant hands-on experience with Databricks and cloud-based data platforms.

A strong candidate should be capable of operating at both strategic and hands-on technical levels, providing architectural direction while remaining close enough to the engineering detail to guide implementation and resolve complex technical challenges.

GCS is acting as an Employment Business in relation to this vacancy.

Read less

Snowflake Data ArchitectRole Overview

We are seeking an experienced Snowflake Data Architect to design, develop, and govern scalable, secure, and high-performing cloud data platforms. The ideal candidate will have strong expertise in Snowflake, data architecture, cloud technologies, data modelling, and modern data engineering practices.

The Snowflake Data Architect will work closely with data engineers, analysts, business stakeholders, and technology teams to define data architecture strategies and deliver robust data solutions that support analytics, reporting, data science, and business intelligence initiatives.

Key ResponsibilitiesDesign and implement scalable, secure, and high-performance data architectures using Snowflake.Define enterprise data architecture, data models, data flows, integration patterns, and technology standards.Develop conceptual, logical, and physical data models aligned with business and analytical requirements.Design Snowflake databases, schemas, tables, views, stages, streams, tasks, and other platform components.Define and implement data ingestion and integration patterns for batch and real-time data.Develop and optimise ELT/ETL pipelines integrating data from a variety of source systems.Establish best practices for Snowflake performance optimisation, workload management, storage, and compute utilisation.Design security and access-control frameworks, including roles, privileges, data masking, row-level security, and other governance capabilities.Define data governance, metadata management, data quality, lineage, and lifecycle management practices.Support data migration and modernisation initiatives, including migration from legacy data warehouses and on-premises platforms to Snowflake.Evaluate and recommend appropriate Snowflake features and cloud technologies based on business and technical requirements.Collaborate with data engineering teams to establish development, deployment, and operational standards.Contribute to CI/CD, DevOps, automation, and infrastructure-as-code practices for data platforms.Troubleshoot complex architectural, performance, scalability, and data integration issues.Produce and maintain architecture documentation, standards, design patterns, and technical specifications.Provide technical leadership and mentoring to data engineers and other technical team members.Stay current with developments in Snowflake, cloud data platforms, data engineering, and modern data architecture.Required Skills and ExperienceStrong experience designing and implementing enterprise-scale data platforms.Extensive hands-on experience with Snowflake and its architecture.Strong understanding of data warehousing, dimensional modelling, data lakes, data lakehouses, and modern cloud data architectures.Experience with SQL and database technologies.Strong understanding of ETL/ELT concepts and data integration patterns.Experience with cloud platforms such as AWS, Microsoft Azure, or Google Cloud Platform.Knowledge of data security, governance, privacy, and access-control principles.Experience with data modelling tools and techniques.Understanding of distributed data processing and large-scale data environments.Experience working with APIs, databases, files, streaming platforms, and other data sources.Familiarity with data engineering and orchestration technologies such as dbt, Apache Airflow, Azure Data Factory, AWS Glue, or similar tools.Experience with source control, CI/CD, and DevOps practices.Strong analytical, problem-solving, and communication skills.Ability to translate business requirements into scalable technical architecture.Desirable SkillsSnowflake certifications or relevant cloud certifications.Experience with Snowflake features such as Snowpipe, Snowpipe Streaming, Streams, Tasks, Dynamic Tables, Snowpark, and Secure Data Sharing.Experience implementing data mesh, data fabric, or lakehouse architectures.Experience with real-time/streaming technologies such as Kafka.Experience with dbt and modern analytics engineering practices.Knowledge of data cataloguing

GCS is acting as an Employment Business in relation to this vacancy.

Read less

About the Role

We're looking for a Senior Ruby on Rails Support Developer to join a fast-paced product engineering team. This role is focused on production support, troubleshooting, and issue resolution within a complex Rails application.

You'll investigate technical issues, work with production data, and deliver practical solutions to business-critical problems. While there may be opportunities to contribute to feature development, your primary focus will be maintaining and supporting a live platform.

Key Responsibilities

Investigate and resolve production support issues involving application code and data.Diagnose root causes using logs, monitoring tools, and database analysis.Troubleshoot configuration issues and implement fixes.Identify, document, and resolve software defects.Analyse and correct data integrity issues, including scripting where required.Collaborate with engineering and support teams to improve platform reliability.Contribute to reducing technical debt and enhancing operational efficiency.

What We're Looking For

Commercial experience with Ruby and Ruby on Rails.Strong troubleshooting and problem-solving skills.Experience supporting production applications and resolving live issues.Comfortable working with databases, logs, and production data.Ability to work independently and own issues through resolution.Strong communication skills and a customer-focused mindset.

Nice to Have

Experience in fast-growing product environments.Exposure to data repair, scripting, and operational support.Familiarity with AI-assisted development tools such as Cursor, Claude, or Codex.Experience balancing support work with occasional feature development.

GCS is acting as an Employment Business in relation to this vacancy.

Read less