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Senior Machine Learning Engineer
Location: Abu Dhabi, UAE - Remote Global
Type: Contract

Role Summary
We are seeking a Senior Machine Learning Engineer to lead the development and deployment of advanced AI models. In this role, you will be responsible for the end-to-end lifecycle of machine learning systems, from architectural design and data preprocessing to model training, optimization, and production deployment.

You will work at the intersection of generative AI and traditional machine learning, building the engines that power automated requirements engineering via LLMs and predictive risk scoring and demand forecasting solutions. Operating within a structured "Sprint Zero" to "Stage Gate" delivery model, you will ensure models are not just accurate, but also robust, explainable, and deployable within strict security environments.

Key Responsibilities

LLM & NLP PipelinesRegulation Parsing: Design and fine-tune Large Language Model (LLM) pipelines to interpret complex regulatory texts (e.g., military standards, building codes) and extract structured rules.Rule Formalization: Convert natural language requirements into computer-processable formats (e.g., logic tuples) that can be executed by downstream compliance engines.Semantic Search: Implement RAG (Retrieval-Augmented Generation) architectures to enable semantic querying of technical documentation and historical project data.Prompt Engineering: Optimize prompt strategies (few-shot learning, chain-of-thought) to improve model performance on domain-specific tasks without extensive retraining. Predictive & Analytical ModelsForecasting Engines: Develop time-series forecasting models to predict material demand and spend categories, integrating internal ERP data with external market signals.Risk Scoring: Build classification and anomaly detection models to assess supplier risk profiles based on financial health, delivery performance, and geopolitical factors.Optimization Algorithms: Design algorithms for multi-objective optimization (e.g., balancing cost vs. lead time vs. risk) to support procurement decision-making. MLOps & ProductionizationModel Deployment: Containerize models using Docker/Kubernetes and deploy them into secure, on-premises inference environments.Pipeline Orchestration: Build automated training and inference pipelines using tools like Kubeflow or MLflow to ensure reproducibility and scalability.Performance Optimization: Optimize model inference latency and resource usage (e.g., quantization, distillation) to run efficiently on available hardware.Monitoring & Retraining: Implement monitoring systems to track model drift and performance in production, establishing feedback loops for continuous improvement.

Technical Requirements

Core ML/AI: Expert proficiency in Python and standard ML libraries (PyTorch, TensorFlow, Scikit-learn, Pandas, NumPy).NLP & GenAI: Strong experience with transformer architectures (BERT, GPT, Llama) and NLP frameworks (Hugging Face, LangChain).MLOps: Proficiency with MLOps tools and practices, including containerization (Docker), orchestration (Kubernetes), and experiment tracking (MLflow).Data Handling: Ability to design data preprocessing pipelines for both structured (SQL, tabular) and unstructured (text, PDF) data.Algorithm Design: Strong grasp of algorithmic principles for implementing custom logic, such as graph traversal or geometric computations.

Professional Qualifications

Experience: 5+ years of experience in Machine Learning Engineering, with a proven track record of deploying models into production environments.Domain Adaptability: Ability to quickly learn and apply ML techniques to specialized domains like defense engineering, supply chain, or construction.Structured Delivery: Experience working in agile environments (Sprints) while adhering to rigorous engineering standards and documentation requirements.Collaboration: Strong communication skills to work effectively with Data Scientists, Backend Engineers, and Domain Experts to align technical solutions with business needs.

Why This Role?

You will be building the intelligence that drives critical national infrastructure. Your models will not just generate text or predictions; they will directly influence the design of complex systems and the resilience of supply chains. If you are ready to apply advanced ML to tangible, high-stakes problems in a rigorous engineering environment, this role is for you.

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

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Senior Machine Learning Engineer - Predictive Modeling

We're expanding an ML team focused on applying machine learning to real-world construction and operational problems.

We're looking for a Senior Machine Learning Engineer who can step into an existing production environment, understand how the models work, identify areas for improvement, and help build new predictive capabilities.

The team currently has a production model that predicts construction job duration and is looking to strengthen that model while developing additional models around permits, costs, and automated construction design.

What Makes This Role Interesting?

You won't be starting from a blank page.

There is already a production model performing well across approximately 80% of use cases. Your challenge will be understanding the remaining 20%:

Why are those predictions inaccurate?Is additional feature engineering needed?Are there specific use cases that require a specialized model?What additional historical signals could improve prediction accuracy?How can the model be retrained and kept current?

You'll also have the opportunity to work on new predictive AI problems.

What You'll DoOwn and enhance production machine learning models.Analyze model performance and identify areas for improvement.Develop feature engineering strategies using historical and geographic data.Build automated retraining pipelines.Develop predictive models using algorithms such as XGBoost.Work with time-based and historical trends to improve predictions.Evaluate factors such as county, ZIP code, job type, seasonality, holidays, and historical processing times.Develop models for construction duration and permitting.Contribute to predictive cost modeling using historical labor and material data.Explore ML approaches for automating construction design.Work with engineers and the broader team to productionize ML solutions.What You BringStrong Machine Learning Engineering background.Experience developing predictive models.Strong Python skills.Experience with XGBoost or boosting algorithms.Feature engineering experience.Experience analyzing model performance and improving accuracy.Experience building ML pipelines and production systems.AWS experience.Experience with MLflow, DVC, or similar ML tooling.Strong understanding of supervised machine learning.Team & Technology

The current team includes ML resources in both the U.S. and India and is continuing to grow. The environment is AWS-based and uses HashiCorp Nomad, FastAPI, DVC, MLflow, and Python.

If you're an ML Engineer who likes digging into data, figuring out why a model isn't working, and turning those findings into better production models, we'd love to hear from you.

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

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Senior Software Engineer - Data, Machine Learning & Applications

Job Summary

We are seeking a versatile Senior Software Engineer with experience in application development, databases, data processing, and Machine Learning systems. This role will focus on designing and building robust, scalable technical solutions while ensuring high standards for software quality and performance.

The ideal candidate combines strong software engineering fundamentals with experience working with relational databases, SQL, data solutions, and modern application development technologies.

ResponsibilitiesDesign, develop, test, and maintain scalable software and Machine Learning applications.Write clean, efficient, and well-documented code that meets performance and business requirements.Participate in application architecture, system design, and technical solution development.Design and build relational databases for application and data processing needs.Write and maintain SQL statements for data retrieval, insertion, and processing.Support data acquisition, data warehousing, archival, and recovery strategies.Evaluate data sources and ensure data quality and integration standards are met.Collaborate with customers, business analysts, and technical teams to understand requirements.Develop and execute testing strategies to ensure reliable software delivery.Track defects, test results, and quality metrics.Identify opportunities to improve software processes, application performance, and technical solutions.Participate in code reviews and recommend improvements to existing systems.Support applications throughout the full software development lifecycle.Required QualificationsBachelor's degree in Computer Science, Information Systems, or a related field, or equivalent professional experience.8+ years of experience in software development, systems engineering, testing, data engineering, or related areas.Strong programming experience with technologies such as:Java.NETNode.jsRubyAngularJSStrong SQL and relational database experience.Experience designing, developing, or supporting scalable software applications.Strong object-oriented design skills.Experience with software testing and quality assurance practices.Ability to work across application development, data, and business teams.Excellent analytical and problem-solving skills.Preferred QualificationsExperience building or supporting Machine Learning systems.Experience with data warehousing or large-scale data processing.Experience evaluating and integrating new data sources.Experience with system architecture and application performance optimization.

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

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Job Description

- Implements, refines, and validates machine learning algorithms for products and applications. - Implements data pipelines consisting of data ingest, data validation, data cleaning, and data monitoring.

- Trains machine learning models, validates the accuracy of the machine learning models once trained, and deploys validated machine learning models into production.

- Assists in development of proof of concept solutions and contributes to studies to support future product or application development.

- Researches, writes, and edits documentation and technical requirements, including evaluation plans, confluence pages, white papers, presentations, test results, technical manuals, formal recommendations, and reports.

- Tests and evaluates solutions. Completes case studies, testing, and reporting.

Skills

- Bachelor's degree in computer science, computer engineering, mathematics, related technical discipline, or related industry experience

- Experience with machine learning, deep learning, data mining, and/or statistical analysis tools and how to deploy and monitor machine learning models.

- Strong programming and software development skills and familiarity with Python, Java or Scala.

- Knowledge of data pipeline and cloud technologies such as Kafka, Spark, and Docker.

- 1-3 years related experience after Bachelors.

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

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