Senior Machine Learning Engineer

What you’ll be doing

Lead, mentor, and develop you engineering team, fostering a culture of learning and collaboration.
Architect and build on BT’s MLOps stacks for fast, safe, and scalable ML/GenAI delivery with clear FinOps guardrails.
Design and implement production-grade ML/AI infrastructure, championing reusable patterns and best practices with Data Scientists, support, and engineering teams.
Embed FinOps, security, and data privacy into every stage of the ML/AI lifecycle.
Work closely with data scientists, engineers, and stakeholders to accelerate research-to-production using robust engineering practices and AI coding tools.
Define support strategies for long-term model health, including SLOs, drift monitoring, and feedback loops.
Lead deployment of LLM and GenAI services on platforms like Amazon Bedrock and Google Vertex AI.
Design and translate infrastructure for GenAI applications: vector databases, embeddings, retrieval/RAG, model gateways, GPU management, observability, and cost monitoring.
Promote experiment tracking and model management tools (e.g., Weights & Biases).
Ensure strong software engineering practices: code review, testing, documentation, and version control. 

Skills

Bachelor’s degree, MSc, or equivalent in Computer Science, Engineering, Mathematics, or related field.
Professional certifications in AWS, GCP, or Azure (Architect, Engineering, or ML) are highly desirable.
Solid experience in ML/AI engineering, cloud engineering, or MLOps
Deep expertise in at least one major cloud platform (AWS, GCP, or Azure); 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.
Experience leading, mentoring, and developing a positive engineering team culture.

Our leadership standards

Looking in:

Leading inclusively and Safely

I inspire and build trust through self-awareness, honesty and integrity.

Owning outcomes

I take the right decisions that benefit the broader organisation.

Looking out:

Delivering for the customer

I execute brilliantly on clear priorities that add value to our customers and the wider business.

Commercially savvy

I demonstrate strong commercial focus, bringing an external perspective to decision-making.

Looking to the future:

Growth mindset

I experiment and identify opportunities for growth for both myself and the organisation.

Building for the future

I build diverse future-ready teams where all individuals can be at their best.

Benefits

Competitive salary
25 days annual leave (plus bank holidays)
10% on target bonus
Life Assurance
Pension scheme
Direct share scheme
Option to join the Healthcare Cash Plan or other benefits such as dental insurance, gym memberships etc.
50% off EE mobile pay monthly or SIM only plans
Exclusive colleague discounts on our latest and greatest BT broadband packages
BT TV with TNT Sports and NOW Entertainment
50% discount for friends and family on EE SIM Only plans & airtime element off a Flex Pay plan