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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior MLOps Engineer - AWS AI Platform (Contract - 6 Months) - **Company:** Talenzon group - **Location:** London, UK - **Experience:** Expert - **Contract:** Temporary contract - **Skills:** Artificial Intelligence, Amazon Web Services, Amazon S3, Audit Trail, Github, Identity and Access Management, Python (Programming Language), Machine Learning, Azure Machine Learning, Delivery Pipeline, Large Language Models, Grafana, Generative AI, Build Management, AI Platforms, Kubernetes, Low Latency, Deployment Automation, HuggingFace, Machine Learning Operations, Cloudwatch, Terraform, Software Version Control, Docker - **Published:** September 10, 2026 - **Apply:** https://www.collegerecruiter.com/job/2840588644-senior-mlops-engineer--aws-ai-platform-contract--6-months ## About the Role # Senior MLOps Engineer - AWS AI Platform (Contract - 6 Months)London, United KingdomJuly 15, 2026ContractApply Now### Job Description**Location:** London, UK **Work Model:** On-site **Contract Duration:** 6 MonthsWe 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**During this engagement, you will:* 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---**Location:** London, UK **Work Model:** On-site **Contract Duration:** 6 Months #J-18808-Ljbffr ## Related Videos - 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