AWS Engineer (Python) in Newcastle upon Tyne

We are looking for a Senior ML Engineer to take technical ownership of our machine learning production environment. Your mission is to build the "highway" that allows our data science team to deploy models rapidly while ensuring those models are observable and fiscally responsible. You will own the entire ML lifecycle—from automated training pipelines to real-time inference clusters—and serve as a key software engineering contributor to our AI product stack. This is a hybrid role – three days per week in our Newcastle office.

Key Responsibilities:

• Design and own the automated "Continuous Training" (CT) and deployment pipelines.
• Architect reusable, modular infrastructure for model training and serving, ensuring the entire lifecycle is versioned and reproducible.
• Lead the team in adopting professional engineering standards, including unit/integration testing, peer code reviews, and applying SOLID principles to ML codebases.
• Establish and own the telemetry framework for the AI stack, implementing proactive monitoring for system health and model-specific metrics.
• Own the strategy for AI cloud spend, building monitoring and alerting frameworks to track compute costs and implement optimization strategies.
• Act as a lead software engineer to integrate models into the product ecosystem, developing high-performance, secure APIs and microservices.
• Own the versioning strategy for the "Holy Trinity" of ML: code, data, and model artifacts, ensuring clear documentation and audit trails for all production deployments.

Required Skills:

• Strong software engineering fundamentals, including production-quality Python, testing, CI/CD practices, and version control.
• Experience designing and operating reliable, versioned REST APIs using an API-first approach.
• Experience building, deploying, and operating backend services in cloud environments, with AWS as the primary platform.
• Experience using containerisation and modern deployment approaches, including Docker, automated pipelines, and basic observability.
• Ability to work effectively with real-world data and production systems in collaboration with product, data, and platform teams.
• Hands-on experience delivering machine-learning systems in production or a strong software-engineering background with motivation to grow into ML and MLOps.

Desirable Skills:

• Using AWS SageMaker for training, deploying, and operating machine-learning workloads.
• Exposing machine-learning models via APIs and operating them reliably at scale.
• Applying MLOps practices, including model and version management, monitoring, and handling model or data drift.
• Implementing advanced service patterns such as asynchronous processing, event-driven architectures, or multi-version services.
• Serving LLM or GenAI-based capabilities in production, including model serving, RAG pipelines, and inference controls.
• Designing reusable, platform-level services and shared ML patterns rather than one-off implementations.
• Managing cloud operational trade-offs, including cost efficiency, latency, scalability, and reliability.