Machine Learning Engineer designing, building, and operationalizing AI/ML solutions for mission - critical applications. Collaborating with data engineering teams to support production - grade ML systems in a hybrid work environment.
About the role
- Senior ML Engineer industrialising ML and AI across BT through collaboration and automation. Architecting ML pipelines and solutions while ensuring cost efficiency and security.
Responsibilities
- You'll play a pivotal role in industrialising ML and AI across BT
- Collaborating with diverse teams to deliver scalable, secure, and high-impact solutions
- You'll architect and automate robust ML/AI pipelines
- Formulate real-time APIs and batch systems that scale, solving operational challenges like zero-downtime model updates, drift monitoring, incident response, and automated retraining
- Ensuring systems are secure, cost-efficient, compliant, and smoothly transitioned into support
- You'll accelerate ML productionisation by building infrastructure and tooling that enable data scientists to deploy models reliably, ensuring they work smoothly in production
- As a senior figure in the ML Engineering team, you’ll provide guidance, solve deployment challenges, and help business units realise value from AI initiatives faster
Requirements
- Bachelor’s degree, MSc, or equivalent in Computer Science, Engineering, Mathematics, or related field
- Professional certifications in AWS and/or GCP (Architect, Engineering, or ML) are highly desirable
- 5+ years in ML/AI engineering, including a minimum of 3+ years of hands-on experience in MLOps
- Deep expertise in at least one major cloud platform (AWS, GCP); knowledge of Vertex AI or equivalent required
- Proven experience building, debugging, and deploying ML pipelines for large-scale, high-throughput, low-latency applications
- Production-level fluency managing components in Python, Docker, and deploying ML/AI services (e.g., FastAPI)
- Supporting skills in SQL and advanced use of Terraform, Pulumi, or AWS CDK
- Advanced expertise in CI/CD pipelines (GitLab CI, GitHub Actions) and MLOps pipelining services (Kubeflow, TFX, Kedro, or MLflow)
- Practical experience deploying LLMs and other AI models, with understanding of sourcing, performance, quantization, batching, inference service management, metrics, and design trade-offs
- Demonstrated experience managing FinOps, security, and data privacy in ML/AI systems
- Proven ability to work directly with data scientists, stakeholders, and as part of Agile squads
- Experience leading, mentoring, and developing a positive engineering team culture
- Personal commitment to continuous learning and professional development
Benefits
- myriad opportunities to learn
- develop
- expand your network
- Continuous learning is essential
- encouraged to keep your skills sharp
- explore new tools
- share knowledge with the team
- Cross-training on parallel platforms is available
Job title
Job type
Experience level
Salary
Not specified
Degree requirement
Tech skills
Location requirements
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