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

Talenzon group; Greater London; On-site; GBP 140,000 - 210,000; Full time

Job summary

Talenzon group is seeking a Senior MLOps Engineer to join our London-based on-site team for a 6-month contract. You will design and implement an enterprise MLOps platform on AWS, deploy production-ready LLM applications and automate model deployment pipelines.

You will build AI monitoring, governance, and CI/CD workflows, plus standardize the ML lifecycle across teams. Strong English communication and hands-on experience with SageMaker, EKS, and vector databases are required.

Qualifications

• 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 and with vector databases.
• Understanding of RAG architectures and English communication skills.

Responsibilities

• Design and build an enterprise MLOps platform on AWS.
• Develop reusable infrastructure supporting model training, validation and deployment.
• Configure SageMaker Pipelines and Model Registry; deploy scalable endpoints.
• Build CI/CD pipelines for ML using GitHub Actions and automate testing, deployment and rollback.
• Establish governance, monitoring dashboards and observability for production AI systems.

Skills

MLOps

AWS SageMaker

Kubernetes

Python

Docker

Terraform

GitHub Actions

Tools

Docker

Kubernetes

Terraform

GitHub Actions

MLflow

LangChain

LangSmith

Job description

# 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