23 of 23 Contract MLOps Jobs in the UK

Search , and custom vector stores. Engineer domain-tuned embeddings using ADA-002, Gecko, Word2Vec, BERT, Sentence Encoder, and topic modelling. AI/ML Pipelines & MLOps Develop scalable AI/ML microservices using Docker, Kubernetes (EKS/GKE), and CI/CD-driven automation. Build and enhance pipelines for model evaluation ...

preferred. 3-5+ years' professional experience in data science with a proven track record of delivering impactful solutions. Strong understanding of CRISP-DM, MLOps, Agile delivery and ITIL or similar frameworks. Expert-level proficiency in Python and SQL, with sound software engineering practices (modular design, testing, CI/ ...

integrations that embed AI into customer journeys, internal tools, and digital platforms* Own model lifecycle processes including deployment, monitoring, evaluation, and retraining* Implement robust MLOps practices to ensure reliability, performance, and scalability* Mentor engineers and contribute to shared tooling, reusable patterns, and engineering standards Required Experience* Strong software engineering background ...

Skills and Qualifications Experience working with cloud platforms such as Azure, AWS, or Google Cloud. Background in deploying machine learning models into production environments (MLOps experience is advantageous). Hands-on experience with big-data or distributed computing tools such as Spark and Databricks. Familiarity with visualisation tools such ...

analytical, data science and AI projects. - Build, maintain and optimise data pipelines, analytical models and data architecture. - Develop and operationalise machine learning solutions, applying MLOps best practice. - Produce high-quality, accessible dashboards and tools using platforms such as Power BI. - Work closely with senior leaders to shape strategic insight, policy ...

Experience with machine learning frameworks including TensorFlow, PyTorch or Scikit-learn. Familiarity with cloud platforms such as AWS, Azure or Google Cloud and associated MLOps practices. Experience with automation and orchestration tooling such as UiPath, Airflow or Kubeflow. Experience and Capability Strong experience delivering AI or automation solutions within enterprise ...

R. Experience with machine learning frameworks and libraries (e.g. TensorFlow, PyTorch, Scikit-learn). Familiarity with cloud platforms (AWS, Azure or Google Cloud) and MLOps practices. Knowledge of automation technologies including RPA platforms (e.g. UiPath) and orchestration tools (Airflow, Kubeflow). Soft Skills: Excellent problem-solving and analytical capabilities. Strong ...

stakeholders. At least 3 years of experience in AI solution engineering. Large Language Models experience including prompt engineering and RAG implementations. Expert data analytics, MLOps practices and API development. Desirable knowledge in Docker and Kubernetes ...

Cloud Functions/Cloud Run Solid Python experience for ML and data engineering workloads Experience productionising models (not just experimentation or notebooks) Understanding of MLOps concepts: CI/CD, monitoring, retraining, and model governance Ability to work independently in a contract environment and deliver at pace Nice to Have Experience ...

issues and implement effective resolutions. Optimize application performance using Azure monitoring tools (Azure Monitor, App Insights). Improve SQL query performance for applications and MLOps workflows. Perform proactive monitoring, troubleshooting, and enhancement of application functionality. Integration & Documentation Integrate third-party APIs/services ensuring seamless communication. Document technical decisions, architectural ...

Role/Job Title: AI & ML Senior Engineer Work Location: London The JJ Mack Building, 33 Charterhouse St, EC1M 6HA Mode of Working: Hybrid If Hybrid, how many days are required in office?: Minimum 2 ...

cloud computing (ideally AWS) Strong experience across industries in both Geospatial and non-Geospatial domains Experience with Machine Learning (sci-kit learn, tensorflow, metaflow, MLOps) Preferred Experience: Experience with frameworks like Metaflow, Prefect, etc. Experience with geospatial libraries i.e. Raster, Geo-pandas, Vector databases Bright Purple is an equal opportunities ...

bring on a DV Cleared Data Architect for a critical defence project. The role is based onsite in Cambridge. Skills: - Python - ML Engineering & MLOps - CI/CD knowledge - ETL - Active DV Clearance This position has a 1 stage interview process. DV Cleared Data Scientist - Cambridge - £600- £700pd Inside IR35 ...

ongoing BAU support and maintenance of data structures, schema, reporting etc. Work with the broader Engineering community to develop the client's data and MLOps capability infrastructure Ensure data quality, governance, and compliance with internal and external standards. Monitor and troubleshoot data pipeline issues, ensuring reliability and accuracy. ...

Lead the implementation of Generative AI models, LLMs, and agentic frameworks. Collaborate with stakeholders to define problem statements and solution approaches. Deploy models using MLOps tools such as SageMaker, Snowflake, and CI/CD pipelines. Integrate vector databases and Retrieval-Augmented Generation (RAG) pipelines. Ensure high-quality delivery and adherence … Azure, GCP) and their AI/ML services. Minimum 5 years in ML model development, data engineering, and software engineering. Minimum 3 years in MLOps and AI/ML deployment. Minimum **2 years in Generative AI, LLMs, and agentic frameworks. Preferred Experience with API Gateway development and deployment on Azure ...

technical and non-technical stakeholders Eligible for or holding SC clearance Desirable Experience: Familiarity with government AI standards, frameworks, or compliance requirements Experience with MLOps, monitoring, and evaluation of AI systems Prior consulting experience in central government or highly regulated sectors Reasonable Adjustments: Respect and equality are core values ...

Role Summary We are seeking a highly skilled MLOps Engineer to focus on the deployment, monitoring, and maintenance of machine learning models in production environments. This role is platform-focused and does not involve model development or end-user support. The successful candidate will ensure reliability, scalability, and performance ...

optimized architectures for data processing, feature engineering, model training, and inference pipelines. Establish standards, patterns, and best practices for platform components including Lakehouse architecture, MLOps, and data governance. Platform Engineering Architect and guide implementation of Databricks workspace setup, clusters, Unity Catalog, Delta Lake, workflows, and model serving. Lead the integration … Step Functions, CloudWatch, and networking components. Ensure platform scalability, reliability, observability, and high availability. Oversee IAM policies, security controls, and data protection mechanisms. MLOps & AI Capabilities Define architecture for end-to-end ML lifecycle, including model development, experiment tracking, CI/CD, deployment, and monitoring. Evaluate technologies and frameworks ...

model reliability. Apply best practices in model evaluation, mitigating data leakage, and managing the bias/variance trade-off. Deploy and monitor models using MLOps approaches (Docker, CI/CD, cloud platforms like AWS/Azure/GCP). Collaborate closely with engineering and product teams to integrate ML solutions … model development, training, validation, and optimization. Proven experience deploying and maintaining production ML models. Hands-on experience with cloud platforms (AWS, Azure, GCP) and MLOps practices. Demonstrated ability to work with complex real-world datasets and perform feature engineering. Familiarity with modern ML frameworks and libraries, including PyTorch, TensorFlow, HuggingFace ...

role in transforming and modernising the organisation’s machine learning platform. This role involves collaborating across the business to define and implement MLOps best practices, as well as designing, building, and maintaining the infrastructure required for developing and deploying machine learning models. This is initially a 6 month contract with … machine learning models into production systems, driving innovation and operational excellence within the client. Responsibilities: Contribute to the design and the development of robust MLOps, and Agentic Ops frameworks that will enhance our capabilities and drive value across the company. Take offline models data scientists build and turn them into ...

This is a fantastic opportunity to work as an MLOps Engineer on a remote basis, outside IR35, for a major pharmaceutical company. The key skills required for this MLOps Engineer position are: MLOps Image data AWS PyTorch, TensorFlow, JAX If you do have the relevant experience for this long term … remote contract MLOps Engineer role, outside IR35, please do apply. ...

Define and execute the enterprise AI & automation roadmap Identify high-value use cases (personalisation, dynamic pricing, forecasting, workforce optimisation) Own AI/ML platforms, MLOps and LLM architecture (Azure preferred) Lead end-to-end delivery from experimentation to scaled deployment Build and lead a multi-disciplinary AI team Embed responsible … scale AI/ML and intelligent automation Strong hands-on knowledge of cloud AI platforms, data engineering and LLM/RAG architectures Experience implementing MLOps, observability and model governance Commercially minded, able to link AI outcomes to revenue, efficiency and customer impact Background within consumer-led/B2C environments ...

Architect (Industry-Focused) Role Purpose Lead customer conversations and architect enterprise AI foundation stacks Stitch enterprise AI architecture & solutions, set up governance & LLMOps/MLOps Train talent convert prioritized use cases into MVPs across one or more of Retail, CPG, Travel-Transportation-Hospitality (TTH), Life Sciences Health Care, Manufacturing … Lead C-suite architecture conversations, value cases, sovereignty considerations. - Define AI foundation stack (infra, platform, model registry, vector DB, guardrails). - Establish LLMOps/MLOps standards integrated with SDLC/DevSecOps. - Embed Responsible AI governance and risk controls. - Prioritize use cases, run solution spikes and deliver MVPs alongside engineering squads ...