Senior MLOps Engineer – AWS AI Platform (Contract – 6 Months) in London

**Location:** London, UK

**Work Model:** On-site

**Contract Duration:** 6 Months

We are looking for a Senior MLOps Engineer with strong experience in machine learning infrastructure and AWS AI services to join our client’s on-site team in London on a 6-month contract. This project focuses on productionising multiple Generative AI and Machine Learning solutions by designing a scalable MLOps platform, implementing automated deployment pipelines, and establishing governance and monitoring standards for enterprise AI systems.

## **Project Objectives**

• Build an enterprise MLOps platform on AWS
• Deploy production-ready LLM applications
• Implement automated model deployment pipelines
• Build AI monitoring and governance capabilities
• Standardise the ML lifecycle across multiple engineering teams
• Deliver operational documentation and knowledge transfer

## **What You’ll Do**

### AWS AI Platform

• Design and build an enterprise MLOps platform using Amazon SageMaker
• Develop reusable infrastructure supporting model training, validation and deployment
• Configure SageMaker Pipelines and Model Registry
• Deploy scalable inference endpoints using SageMaker Endpoints

### LLM Infrastructure

• Deploy containerised AI workloads on Amazon EKS
• Build Retrieval-Augmented Generation (RAG) pipelines
• Integrate vector databases including Pinecone and Amazon OpenSearch Vector Engine
• Optimise GPU workloads for low-latency inference

### ML CI/CD

• Build CI/CD pipelines for machine learning using GitHub Actions
• Automate model testing, validation, deployment and rollback
• Implement model versioning and artifact management
• Standardise deployment workflows across engineering teams

### Monitoring & AI Operations

• Monitor model performance, latency and drift
• Implement observability using Amazon CloudWatch, Grafana, MLflow and LangSmith
• Build dashboards for production AI services
• Support incident management and model performance optimisation

### Governance

• Implement reproducible ML workflows
• Establish approval processes for production models
• Support AI governance, traceability and auditability
• Produce operational documentation and deployment standards

## **What We’re Looking For**

### Required Skills & Experience

• 5+ years of experience in MLOps or Machine Learning Engineering
• Strong experience with AWS AI services including:
• Amazon SageMaker
• Amazon EKS
• IAM
• CloudWatch
• S3
• Strong Kubernetes administration
• Python
• Docker
• Terraform
• GitHub Actions
• MLflow
• LangChain
• LangSmith
• Experience deploying LLMs in production
• Experience with vector databases
• Strong understanding of RAG architectures
• Excellent English communication skills

### Nice to Have

• Amazon Bedrock
• Hugging Face
• NVIDIA Triton Inference Server
• KServe
• Kubeflow
• Pinecone
• OpenSearch Vector Engine
• AWS Machine Learning Specialty Certification

## **Project Deliverables**

• Production-ready Amazon SageMaker platform
• Enterprise MLOps framework
• Automated ML deployment pipelines
• LLM inference platform
• AI monitoring dashboards
• Governance documentation
• Knowledge transfer to internal engineering teams