AI ML Engineer at global networking leader, shaping ML strategy and building high - performance systems. Innovating with AI technology to enhance network management and develop flagship products.
BI
Hybrid Senior Machine Learning Engineer
Posted 2 months ago
About the role
- Senior ML Engineer designing and developing machine learning models for national security. Collaborating with cross-functional teams to deliver scalable solutions in defense applications.
Responsibilities
- 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.
Requirements
- 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 developing LLM/GenAI applications, including prompt engineering and RAG architectures
- Familiarity with LLMOps tooling such as LangSmith, LangChain, or LangGraph
- Understanding of model evaluation, validation techniques, and production monitoring
- Experience working in cross-functional teams from problem framing through to production delivery
- Ability to communicate complex findings to non-technical audiences clearly
- Strong problem-solving skills and knowing when AI is not the answer.
Benefits
- Work-life balance is important; you can work around core hours with flexible and part-time working
- 25 days holiday a year and the option to buy/sell and carry over from the year before
- Our flexible benefits package includes private medical and dental insurance, a competitive pension scheme, cycle to work scheme, taste cards and more
- You’ll have a dedicated Career Manager to help you develop your career and guide you on your journey through BAE
- You’ll be part of our company bonus scheme
- You are welcome to join any/all of our Diversity and Support groups.
Job title
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Degree requirement
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