Machine Learning Engineer

• Design and develop machine learning models for traditional ML use cases (forecasting, classification, anomaly detection) and GenAI/LLM applications
• Lead experimentation cycles: define hypotheses, design experiments, evaluate results, and iterate rapidly while adhering to governance requirements
• Transition validated experiments into production-ready solutions, working closely with other engineers on deployment and monitoring
• Build and optimise ML pipelines using AWS services and experiment tracking tools
• Develop and integrate LLM-powered solutions for tracing, evaluation, and production monitoring
• Implement robust experiment tracking, model versioning, and reproducibility practices with full audit trails
• Design feature engineering approaches and contribute to feature store development
• Support production models through monitoring, performance analysis, and continuous improvement
• Apply responsible AI practices, including model explainability and fairness assessment
• Present experiment findings and production outcomes to stakeholders, articulating operational and strategic value
• Mentor junior colleagues and share learnings across the team

Technologies:

• AI
• AWS
• Lambda
• Docker
• Support
• Kubernetes
• LLM
• Machine Learning
• PyTorch
• Python
• Security
• TensorFlow
• Cloud

• Must hold active DV Clearance
• Hands-on experience developing and deploying ML models in Python using frameworks such as scikit-learn, XGBoost, PyTorch, or TensorFlow
• Strong experience with AWS ML services (SageMaker, Lambda, S3) in production environments
• Strong experiment design skills: hypothesis formulation, A/B testing methodology, and statistical evaluation
• Proven track record transitioning models from experimentation to production with appropriate governance and quality controls
• Experience with experiment tracking and MLOps tooling (MLflow, Weights & Biases, Data Version Control)
• Experience with advanced LLM techniques: agents, tool use, and agentic workflows (preferred)
• Experience with vector databases (Pinecone, Weaviate, pgvector) for RAG applications (preferred)
• Experience with feature stores (Feast, AWS Feature Store) (preferred)
• Experience with containerisation (Docker) and orchestration (Kubernetes, ECS) (preferred)