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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.

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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.

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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.

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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

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Freelance Data Architect (Generalist)The Opportunity

We're looking for a senior, hands-on Data Architect to design and steer enterprise data architecture within a large multinational organisation headquartered in Brussels.

This is not an ivory-tower architecture role. You'll bridge the gap between Business and IT, build consensus across departments, and stay technically credible by building and executing small Proof of Concepts yourself. If you're the kind of architect who can present a target architecture to senior stakeholders in the morning and validate a design decision in Snowflake or Databricks in the afternoon, this is for you.

What You'll DoArchitecture & Strategy - Design, steer, and implement data architecture tailored to a large-scale, multinational organisational structure.Stakeholder Management - Gather requirements from Business and IT stakeholders alike, and build a strong internal network across multiple IT departments.Corporate Navigation - Proactively manage internal dynamics, drive alignment, and build consensus through strong communication.Hands-on Validation - Independently build and execute small PoCs within the existing toolsets to validate architectural decisions and maintain technical alignment.What You'll BringTechnicalProven experience with Informatica or an equivalent enterprise ETL/ELT platformStrong expertise in Kimball (dimensional modelling) and Data Vault methodologiesWorking knowledge of a modern cloud data platform - Snowflake, Microsoft Fabric, or Databricks - is a strong plusExperience & Soft SkillsA proven track record of delivering within large, complex organisationsA mature, proactive working style with strong ownership of outcomesExcellent communication skills and the political awareness to navigate a corporate environment Comfortable operating autonomously while keeping stakeholders alignedLanguagesEnglish: Fluent (must)Dutch or French: Fluent/proficient (must)Why This RoleLong-term engagement with genuine architectural ownership - you set the direction, not just the diagramsHybrid working - 2 days per week in Brussels, remainder remoteWork at the intersection of established enterprise data (Informatica, DWH) and modern cloud platformsA visible role with direct access to senior Business and IT stakeholders

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

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