Senior DevOps Engineer (Infrastructure & MLOps)

Talenzon group; Greater London; On-site; GBP 70,000 - 90,000; Full time

Job summary

A leading tech consulting firm based in London is searching for a Senior DevOps Engineer to join their on-site team. This role centers on designing and managing cloud infrastructure as well as supporting AI/ML platforms. The ideal candidate will have over 5 years of experience in DevOps, strong knowledge in AWS, Docker, and Kubernetes, along with excellent scripting and CI/CD pipeline management skills. This full-time position offers an opportunity to work closely with cross-functional teams on innovative solutions.

Qualifications

• 5+ years of experience in a DevOps, SRE, or infrastructure engineering role.
• Expert knowledge of cloud platforms, preferably AWS.
• Strong scripting skills, Python required.

Responsibilities

• Design, implement, and manage cloud-based platform infrastructure.
• Create and automate deployment pipelines using CI/CD tools.
• Implement MLOps best practices for model transitions.

Skills

AWS

Docker

Kubernetes

CI/CD

Python

Terraform

Monitoring tools (Datadog, Prometheus, Grafana)

Tools

GitHub Actions

GitLab

MLflow

Ansible

Job description

# Senior DevOps Engineer (Infrastructure & MLOps),February 15, 2026### Job Description**Location:** London, UK **Work Model:** On-site **Role Type:** Full-TimeWe are looking for a **Senior DevOps Engineer (Infrastructure & MLOps)** with strong experience in cloud infrastructure and CI/CD automation to join our client’s on-site team in London.This role focuses on designing, implementing, and managing highly available cloud infrastructure while supporting AI/ML platforms and production-grade deployment pipelines. You will work closely with engineering and data teams to deliver scalable, secure, and reliable systems.---### **What You’ll Do*** Design, implement, and manage highly available infrastructure for cloud-based platforms using **Amazon Web Services*** Architect and support AI/ML infrastructure including Lambda-based workloads and managed ML environments for training, hosting, and inference* Create and automate robust deployment pipelines using CI/CD tools such as **GitHub** Actions and **GitLab*** Build, maintain, and scale containerised applications using **Docker** and **Kubernetes** / ECS / Fargate* Implement MLOps best practices to streamline model transition from development to production* Ensure system scalability and reliability through monitoring, logging, and automated alerting using tools such as **Datadog**, **Prometheus**, **Grafana**, and **MLflow*** Collaborate with Product Engineers and Data Scientists to optimise performance, security, and infrastructure costs* Manage and evolve Infrastructure-as-Code environments---### **What We’re Looking For**#### **Required Skills & Experience*** 5+ years of experience in a DevOps, SRE, or infrastructure engineering role* Expert knowledge of cloud platforms (AWS preferred; GCP or Azure also valuable)* Strong experience with containerisation technologies (Docker, ECS, Kubernetes)* Proven experience designing and managing complex CI/CD pipelines* Experience with MLOps workflows (model versioning, retraining pipelines, feature stores)* Hands-on experience with monitoring and logging platforms* Strong scripting skills (Python required; Bash, Go, or similar beneficial)* Experience with Infrastructure-as-Code tools (Terraform, Ansible, or CloudFormation)* Excellent communication skills and ability to collaborate with both DevOps and Data Science teams on-site---#### **Nice to Have*** Direct experience with AWS Lambda and managed ML services* Experience with database systems (SQL/NoSQL) and orchestration of data pipelines including **Google BigQuery*** Knowledge of security and data privacy best practices in regulated environments (e.g. healthcare / sensitive data)* Previous experience working in fast-paced startup or scale-up environments---**Location:** London, UK **Work Model:** On-site **Role Type:** Full-TimeLocation,Experience levelMid–Senior level## Work Location