Curated MLOps roles by location and type: London, USA, China, Contract. We’ll be updating these manually for now.
MLOps role in a large-scale financial environment, focusing on cloud MLOps pipelines, CI/CD, model deployment, and close collaboration with data science and engineering teams.
Design and implement reusable ML templates, deployment patterns, and MLOps tooling for scalable ML solutions. Collaborate with ML teams (Forecasting, Recommendations, Marketing, etc.), drive standardisation, CI/CD for ML, model registries, monitoring, and feature management. Azure, Python, MLflow, Docker/Kubernetes. Hybrid (2+ days/week in office).
Build and scale ML pipelines, data platforms, and production-ready models. MLOps, Data Engineering, and DevOps: MLflow, Databricks, Azure, CI/CD, Terraform. PyTorch, TensorFlow, Scikit-learn. 3+ years MLOps/ML engineering. Data consultancy headquartered in Edinburgh, UK.
Build and scale ML infrastructure for next-generation sports analytics. Own MLOps pipelines from raw data to production; CI/CD for ML, retraining automation, monitoring. Collaborate with Data Scientists and ML Engineers. MLflow, Kubeflow, Airflow, Feast, DVC, W&B; Python, AWS/SageMaker. London, Barcelona or remote UK/Spain.
Lead MLOps Engineer – London, permanent. Own ML infrastructure and MLOps foundations; CI/CD for ML, productionise models, Docker/Kubernetes, Terraform/Helm. Staff/lead-level MLOps or DevOps. Python, PyTorch/TensorFlow, AWS/GCP/Azure. Relocation support possible.
Build the firm's core AI and ML platform in the Machine Learning Technology team. Partner with research, trading, and operations to design and deploy AI/ML applications and agents. GenAI and ML; Python, production services, software best practices. Advantageous: TypeScript/React/Next.js, PostgreSQL/Redis, GenAI agents, model fine-tuning. Global alternative investment manager; London HQ.
MLOps engineer role in investment banking, London. View the full job title, company, and description at the link below.
Design, deploy, maintain and refine ML and statistical models using Azure ML for a London financial services client. Model development with actuarial analysts (life expectancy, default risk, investment returns); data pipelines and ETL; model drift monitoring, data-quality alerts, scheduled retraining. Python, CI/CD, DevOps/MLOps; data wrangling with Python, SQL, ADF. Power BI desirable; pensions or regulated financial services experience a plus. £40k–60k. Hybrid (home and London office).
Blockchain intelligence company. Build and scale AI/ML infrastructure for LLMs and agentic systems: CI/CD for training, evaluation, deployment; Langfuse, GitHub Actions; vector DBs, feature stores, model registries; LangChain, LlamaIndex, vLLM, MLflow, BentoML. Python, Docker, Kubernetes, Terraform. 5+ years Data/ML Engineer experience. US base salary range $200k–$220k.
Senior/Staff ML Platform Engineers for physical-world autonomy (robotics, construction). Build labeling and evaluation pipelines, data mining, experiment tracking, scaled training; MLOps frameworks and best practices. Ray, Kubeflow, MLflow, Metaflow, Airflow, Feast, Vertex, SageMaker. 5+ years ML platform/infra. NY or SF.
Senior MLOps Engineer role at LVT. Remote position. View the full job description and details at the link below.
Software Engineer role in Apple's AIML organization, focused on Machine Learning Platform and Infrastructure. View the full job description and details at the link below.
Contract MLOps-related role. View the full job title, company, location, and description at the link below.
Contract: 5 months (extendable). 350–400 GBP/day (Inside IR35). Redis cluster setup; Kafka/Flink streaming pipelines; S3 data pipeline; real-time micro-batches (5 min, hourly, daily); Mongo/Atlas or S3; SageMaker MLOps, training & model deployment; PyTorch. Apply via recruiter (link below).
Contract: initially 3 months. £500–550/day Inside IR35. Consultancy-led team building production-grade ML platforms for end clients. End-to-end MLOps platforms, MLflow (experiment tracking, model registry), production deployments, CI/CD for ML, model governance and monitoring, Databricks, AWS/SageMaker, Docker/Kubernetes, Terraform, Python. Remote with occasional travel to client-site. Via Artificial Intelligence Jobs UK.
MLOps engineer role focused on building cloud MLOps pipelines, CI/CD orchestration, model deployment, versioning, monitoring, and collaboration with data teams.
MLOps Engineer role at Tekever. View the full job description and details at the link below.
ML Ops Engineer role on the Agentic AI Lab founding team at Fabrion. View the full job description and details at the link below.
Tesla role via Mokahr. View the full job title, description, and location at the link below.
ML Engineer role in investment banking (via LinkedIn). View the full job title, company, location, and description at the link below.