Senior Machine Learning Engineer

BT Group; Highgate; On-site; GBP 70,000 - 90,000; Full time

Job summary

A leading telecommunications company is seeking a Senior Machine Learning Engineer to collaborate with diverse teams and industrialize machine learning and AI across the organization. This role will involve leading the engineering team, designing production-grade ML/AI infrastructure, and implementing FinOps and security measures. The ideal candidate has a strong background in ML/AI and cloud engineering, with expertise in tools such as Terraform and Docker. Competitive salary and extensive benefits are offered.

Benefits

Competitive salary

25 days annual leave plus bank holidays

10% on target bonus

Life Assurance

Pension scheme

Direct share scheme

Healthcare Cash Plan options

Gym memberships

50% off EE mobile plans

Qualifications

• Bachelor's degree or MSc in Computer Science, Engineering, Mathematics, or related field.
• Professional certifications in AWS, GCP, or Azure are highly desirable.
• Solid experience in ML/AI engineering, cloud engineering, or MLOps.

Responsibilities

• Lead, mentor, and develop the engineering team.
• Architect and build on MLOps stacks for scalable delivery.
• Design and implement production-grade ML/AI infrastructure.

Skills

ML/AI engineering

AWS

GCP

Python

Docker

CI/CD pipelines

MLOps

Infrastructure management

Education

Bachelor's degree in Computer Science or related field

Professional certifications in AWS, GCP, or Azure

Tools

Terraform

Pulumi

AWS CDK

Kubeflow

Job description

Overview

BT is transforming data, machine learning, and AI to drive our business and customer experiences. As a Senior Machine Learning Engineer you will industrialise ML and AI across BT, collaborating with diverse teams to deliver secure, high‑impact, scalable solutions.

Responsibilities

• Lead, mentor, and develop the 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 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.

Qualifications

• Bachelor's degree or MSc 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, deploying ML/AI services (e.g., FastAPI); 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.

Benefits

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