1 to 25 of 118 Permanent MLOps Jobs in England

enterprise. Work with a multi-functional team (DevOps, data engineers, developers, testers, Infosec) to productionize AI services on AWS. Enhance our tech stack, including MLOps, CI/CD pipelines, UI, and AI Python libraries. Develop prompts, fine-tune models, and track results. Evaluate third-party GenAI and LLM technologies ...

iterate). · Exposure to: LangChain and LangSmith/LangFuse Supervised fine-tuning or lightweight tuning (LoRA, PEFT) RAG evaluation techniques and model feedback loops MLOps/LLMOps tools (MLflow, Kubeflow, Docker, Kubernetes) · Familiarity with: AgentOps concepts (agent evaluation, monitoring) Basic telemetry/observability workflows (OpenTelemetry) Understanding of how domain-specific ...

Experience working with major cloud platforms such as AWS, Azure, or GCP. Familiarity with containerisation and orchestration technologies (e.g. Docker, Kubernetes). Exposure to MLOps practices or deploying AI/ML models into production environments. Experience working in agile or fast-paced delivery teams . ...

learning frameworks (e.g. TensorFlow, PyTorch) Exposure to cloud platforms (AWS, GCP, or Azure) Experience with experimental design, research methods, or academic publishing Understanding of MLOps, version control (Git), or containerisation (e.g. Docker) Benefits: Competitive Salary & Bonus : £35,000 – £45,000 plus bonus Hybrid Working : A flexible mix of office ...

every model we build is fair, free from bias, and explainable to consumers, customers, and regulators. Engineering & Deployment: Work within the engineering team on MLOps to containerise, deploy, and monitor models in high-scale production. Skills & Requirements Core Data Science: Foundational Stats: You must have an excellent grasp of Linear ...

solutions based on real user feedback and real-world constraints. Learn modern tooling and practices Gain hands-on experience with ML frameworks, cloud infrastructure, MLOps tools, and best practices for building scalable AI systems. Communicate clearly Translate technical findings into clear, structured insights for collaborators across technical and business teams. ...

fast-paced environment Nice to Have Experience within financial services or other regulated industries Exposure to vector databases (e.g. Pinecone, Weaviate, FAISS) Experience with MLOps/LLMOps and deploying models into production Familiarity with LLM fine-tuning, evaluation, or guardrailing techniques Experience building internal tools such as copilots or knowledge ...

/CD and infrastructure as code. • Experience of optimising systems for performance and scalability and reliability. • Understanding of machine learning workflows or MLOps for deploying and monitoring models in production. • Experience with cloud platforms, especially AWS and its data services (e.g., BigQuery, Redshift, Athena). • Knowledge of network protocols ...

Strong technical skills in Python, Java/Go, TensorFlow, PyTorch, and data engineering. Proven ability to engage directly with CxO-level stakeholders. Experience in MLOps, AI governance, and large-scale deployment. Recognised professional certifications in AI or cloud technologies. If you have these skills and would like to find ...

Owning ML models and pipelines that extract structured knowledge from complex, unstructured documents at scale. ▸ NLP · Document AI · Data Pipelines · Evals 3. Staff SE – MLOps/DevOps for AI Shaping the infrastructure behind model serving, training pipelines, LLMOps, and AgentOps across the full AI stack. ▸ MLFlow · Databricks · Datadog · Terraform ────────────────────────── What ...

LLMs, prompt engineering, RAG, vector databases, evaluation, and security. Hands-on experience with fine-tuning, deploying, and monitoring large-scale GenAI systems. Strong MLOps/LLMOps capability: CI/CD, monitoring, governance. Cloud expertise across AWS, Azure, and/or GCP (cloud-agnostic preferred). Ability to communicate complex ...

/CD and infrastructure as code. • Experience of optimising systems for performance and scalability and reliability. • Understanding of machine learning workflows or MLOps for deploying and monitoring models in production. • Experience with cloud platforms, especially AWS and its data services (e.g., BigQuery, Redshift, Athena). • Knowledge of network protocols ...

/CD and infrastructure as code. • Experience of optimising systems for performance and scalability and reliability. • Understanding of machine learning workflows or MLOps for deploying and monitoring models in production. • Experience with cloud platforms, especially AWS and its data services (e.g., BigQuery, Redshift, Athena). • Knowledge of network protocols ...

model artifacts, and inference outputs. Maintain audit trails for model decisions, data access, and deployment history. Attributes Required Qualifications 4+ years of experience in MLOps, ML Engineering, DevOps, or a closely related infrastructure role. Strong proficiency in Python for ML pipeline development, tooling, and automation. Hands-on experience with ...

Azure or Google Cloud** Data platforms including **Databricks, Snowflake or BigQuery** Python and modern ML frameworks Generative AI, LLM integration and RAG pipelines MLOps tooling and modern ML lifecycle management. The profile Experience designing enterprise AI or data platforms in complex technology environments. A background delivering large-scale transformation programmes ...

services. Apply data modelling concepts (e.g., medallion architecture) and ensure data quality and governance. Collaborate with DevOps teams to automate CI/CD and MLOps processes using Azure DevOps, Kubernetes, and Terraform. Visualize and communicate insights using Power BI, Tableau, and PowerApps. Mentor junior engineers and contribute to internal knowledge ...

move from legacy and hybrid platforms to a modern Fabric setup Supporting BI teams with semantic models, datasets and performance optimisation Enabling ML and MLOps workflows, from feature engineering to deployment Working closely with data scientists, analysts, IT and the wider business Embedding data quality, governance and best-practice standards ...

professional accreditation such as CEng. Experience with graph databases and advanced query languages (e.g., Neo4j, Gremlin). Knowledge of machine learning data pipelines and MLOps practices. Familiarity with data virtualisation and data mesh concepts. Hands-on experience with containerisation and orchestration (Docker, Kubernetes, Red Hat OpenShift and Ceph). Exposure ...

Terraform or Azure Bicep) to automate cloud deployments. CI/CD pipelines with Azure DevOps or GitHub Actions for automated deployment of data pipelines. MLOps best practices - MLflow, Databricks Model Serving, Feature Store. Knowledge of IFRS 17, BCBS 239, UK Data Protection Act and Solvency II compliance. Experience with pricing ...

ecosystem components including data lakes, data warehouses, data mesh architectures, and associated architectural patterns. Strong understanding of methodologies and principles of data science and MLOps, including model development, validation, and lifecycle management. Experience in data pipeline design, ETL/ELT processes, and data integration approaches and appreciation of trade-offs ...

priorities, work independently and collaborate effectively with stakeholders. Desirable Skills: Experience with Microsoft Fabric, Azure Machine Learning or similar enterprise cloud platforms. Exposure to MLOps practices, lifecycle management and large-scale or distributed compute environments. Familiarity with agentic AI concepts and frameworks such as LangChain, AutoGen or CrewAI, with ...

Solid understanding of ETL and data engineering best practices Experience with SQL/NoSQL, APIs, and cloud platforms Familiarity with big data tools and MLOps concepts Strong communication, collaboration, and mentoring skills Main Tech Stack: Python AWS (S3, Athena), Terraform, Docker Prefect, Pulsar PostgreSQL, Cassandra GitLab CI/CD What ...

systems . Develop and maintain end-to-end ML pipelines for data ingestion, training, testing, and deployment. Optimise model performance, scalability, and reliability using MLOps best practices. Work with large-scale structured and unstructured datasets for model training and validation. Implement model monitoring, versioning, and retraining processes to ensure continuous … TensorFlow, PyTorch, scikit-learn, pandas, NumPy). Solid understanding of machine learning algorithms , statistical modelling , and deep learning architectures . Hands-on experience with MLOps tools and frameworks (e.g., MLflow, Kubeflow, SageMaker, Vertex AI). Experience with data engineering concepts — ETL pipelines, data lakes, and cloud data platforms. Proficiency with ...

system design. Desirable (AI & Data) Vector DBs: Milvus, Weaviate, Pinecone Data orchestration: Airflow, Prefect, Dagster Search: ElasticSearch/OpenSearch AI frameworks: LangChain, Hugging Face MLOps: MLflow, Kubeflow If you are looking for an opportunity to work on mission-critical systems at the forefront of UK National Security? Apply ...

ontologies (e.g., PyTorch, JAX, Spark, HL7, FHIR) (desirable) Familiarity with cloud infrastructure (Azure/AWS), infrastructure as code, Kubernetes, Linux, Docker, data pipelines, and MLOps tools (desirable) Passion for biomedical topics and startup experience (desirable) What’s on offer Competitive salary, bonus scheme, and strong benefits package Pension, enhanced sick ...