1 to 25 of 64 Permanent MLOps Jobs in the UK excluding London

/GenAI strategies aligned with business objectives. Designing and developing NLP solutions using techniques such as text classification, NER, topic modeling, and text generation. Collaborating with data engineers and MLOps teams to ensure clean data pipelines and seamless model integration. Applying LLMs (e.g., GPT, BERT, LLaMA2) and knowledge graphs to enhance natural language understanding and reasoning. Conducting exploratory data analysis More ❯

scalable AI applications across the 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 for cost-benefit analysis. Deliver projects More ❯

Engineering, Data Science, or related field. Experience with streaming data technologies (e.g., Kafka, Kinesis). Familiarity with data cataloging and metadata management tools. Exposure to machine learning pipelines or MLOps is a bonus. At bp, we provide the following environment and benefits to you: A company culture where we respect our diverse and unified teams, where we are proud of More ❯

Engineering, Data Science, or related field. Experience with streaming data technologies (e.g., Kafka, Kinesis). Familiarity with data cataloging and metadata management tools. Exposure to machine learning pipelines or MLOps is a bonus. At bp, we provide the following environment and benefits to you: A company culture where we respect our diverse and unified teams, where we are proud of More ❯

Engineering, Data Science, or related field. Experience with streaming data technologies (e.g., Kafka, Kinesis). Familiarity with data cataloging and metadata management tools. Exposure to machine learning pipelines or MLOps is a bonus. At bp, we provide the following environment and benefits to you: A company culture where we respect our diverse and unified teams, where we are proud of More ❯

programming, software design, i.e., SOLID principles, and testing practices. Knowledge and working experience of AGILE methodologies. Proficient with SQL. Familiarity with Databricks, Spark, geospatial data/modelling Exposure to MLOps, model monitoring principles, CI/CD and associated tech, e.g., Docker, MLflow, k8s, FastAPI etc. are a plus. Additional Information What’s in it for you?: Competitive salary that reflects More ❯

redefining how advanced AI is deployed across Defence. What You'll Do Lead the design and deployment of machine learning infrastructure and software for Defence programmes. Build scalable, reusable MLOps tools to accelerate project delivery. Work closely with data scientists, engineers, and product teams to integrate advanced AI into mission-critical systems. Engage directly with clients to understand technical requirements More ❯

redefining how advanced AI is deployed across Defence. What You'll Do Lead the design and deployment of machine learning infrastructure and software for Defence programmes. Build scalable, reusable MLOps tools to accelerate project delivery. Work closely with data scientists, engineers, and product teams to integrate advanced AI into mission-critical systems. Engage directly with clients to understand technical requirements More ❯

build, and deploy production-grade ML systems that have a direct impact on government services and policy delivery. What you'll be doing Designing and delivering software, infrastructure, and MLOps systems that bring ML models into real-world use. Collaborating in cross-functional teams with engineers, data scientists, product managers, and designers. Creating scalable, reusable tools that improve ML deployment More ❯

practicing the importance of Secure Software Development. Having skills in technologies such as Kubernetes, Docker and Database engineering (PostgreSQL/DynamoDB) would be desirable but not essential. Exposure to MLOps practices for deploying and monitoring AI models in production would also be highly desirable. Role at a glance: Senior Software Engineer (AI) Fully remote or Hybrid positions available (UK only More ❯

practicing the importance of Secure Software Development. Having skills in technologies such as Kubernetes, Docker and Database engineering (PostgreSQL/DynamoDB) would be desirable but not essential. Exposure to MLOps practices for deploying and monitoring AI models in production would also be highly desirable. Role at a glance: Senior Software Engineer (AI) Fully remote or Hybrid positions available (UK only More ❯

/infrastructure, with at least 5 years leading large teams. Deep expertise in distributed systems, model training infrastructure, and inference platforms. Strong experience with cloud platforms (AWS, GCP, Azure), MLOps/DevOps, and security. Excellent leadership, communication, and stakeholder management skills. Preferred Advanced technical degree (MS/PhD). Strong knowledge of data systems (SQL/NoSQL, Spark, Kafka, Flink More ❯

engineering. Experience in a recent technical lead or architect-level role. Proven delivery of large-scale data systems using cloud platforms (AWS, Azure, or GCP). Deep knowledge of MLOps practices (MLflow, Docker, Kubernetes, etc.). Demonstrated experience in building Data-as-a-Service (DaaS) solutions or data APIs. Strong stakeholder engagement and mentoring skills. Desirable: Experience in insurance, financial More ❯

and transformer-based architectures. Strong Python skills and familiarity with key ML/DL libraries. Experience with Azure (or similar cloud platforms), containerization (Docker/Kubernetes a plus), and MLOps tools. Understanding of healthcare data privacy, compliance (e.g., ISO standards), and secure data handling. Strong communication skills and ability to work cross-functionally in a collaborative environment McGregor Boyall is More ❯

and transformer-based architectures. Strong Python skills and familiarity with key ML/DL libraries. Experience with Azure (or similar cloud platforms), containerization (Docker/Kubernetes a plus), and MLOps tools. Understanding of healthcare data privacy, compliance (e.g., ISO standards), and secure data handling. Strong communication skills and ability to work cross-functionally in a collaborative environment If this sounds More ❯

redefining how advanced AI is deployed across Defence. What You'll Do Lead the design and deployment of machine learning infrastructure and software for Defence programmes. Build scalable, reusable MLOps tools to accelerate project delivery. Work closely with data scientists, engineers, and product teams to integrate advanced AI into mission-critical systems. Engage directly with clients to understand technical requirements More ❯

design, build, and operationalise production-grade AI systems that directly impact high-stakes projects in the UK. What you'll be doing Designing, building, and deploying software, infrastructure, and MLOps systems that leverage machine learning. Working in cross-functional teams with engineers, data scientists, product managers, and designers. Developing scalable, reusable tools that accelerate ML delivery. Providing technical expertise to More ❯

for SC clearance is 5 years continuous residence in the UK up to the present. About the Role You will design, build, and deploy production-grade software, infrastructure, and MLOps systems that leverage machine learning. The work you do will help our customers solve a broad range of high-impact problems in the Defence and National Security arena. What You More ❯

and incident management Hands-on experience enabling AI/ML in a data platform Strong ETL/ELT engineering skills Desirable Experience with Python and related tooling Understanding of MLOps practices (MLflow, Azure ML) Familiarity with real-time data technologies (Kafka, Delta Live Tables) If you're passionate about transforming the banking industry and eager to leverage your expertise to More ❯

and incident management Hands-on experience enabling AI/ML in a data platform Strong ETL/ELT engineering skills Desirable Experience with Python and related tooling Understanding of MLOps practices (MLflow, Azure ML) Familiarity with real-time data technologies (Kafka, Delta Live Tables) If you're passionate about transforming the banking industry and eager to leverage your expertise to More ❯

grade AI/ML solutions and driving adoption at scale. Deep expertise in GenAI (LLMs, prompt engineering, RAG) as well as classical ML and experimentation. Hands-on knowledge of MLOps, cloud (AWS/Azure), and modern data science tools. Exceptional stakeholder skills - able to translate complex technical work into board-level strategy and outcomes. Robert Half Ltd acts as an More ❯

a technical lead role. Deep experience in training and deploying computer vision models into production Proven track record with LLM fine-tuning, prompt engineering and productionisation Deep experience in MLOps on Azure, including CI/CD, monitoring and scaling pipelines. Strong coding skills in Python, with frameworks such as PyTorch, FastAPI and Azure CLI. ALL APPLICANTS MUST BE FREE TO More ❯

a technical lead role. · Deep experience in training and deploying computer vision models into production · Proven track record with LLM fine-tuning, prompt engineering and productionisation · Deep experience in MLOps on Azure, including CI/CD, monitoring and scaling pipelines. · Strong coding skills in Python, with frameworks such as PyTorch, FastAPI and Azure CLI. ALL APPLICANTS MUST BE FREE TO More ❯

a technical lead role. · Deep experience in training and deploying computer vision models into production · Proven track record with LLM fine-tuning, prompt engineering and productionisation · Deep experience in MLOps on Azure, including CI/CD, monitoring and scaling pipelines. · Strong coding skills in Python, with frameworks such as PyTorch, FastAPI and Azure CLI. ALL APPLICANTS MUST BE FREE TO More ❯

model strategy, adaptation, and LLMOps. AI safety, governance, and compliance-especially on Azure. Machine Learning at Scale Applied ML: time-series forecasting, tabular ensembles, NLP, recommendation/propensity models. MLOps: automated pipelines, registries, scoring, monitoring, and safe rollbacks. Responsible ML: bias/fairness testing, explainability, PII controls, human-in-the-loop, EU AI Act readiness. Experimentation: rigorous testing, telemetry, and More ❯