25 of 25 Remote/Hybrid MLOps Jobs in the South East

similar data processing technologies. Experience deploying and operating AI systems in cloud environments using containerisation, managed ML services or serverless architectures. Understanding of MLOps practices including model versioning, experiment tracking, CI/CD for models and monitoring of model performance and drift. Experience applying responsible AI principles, including data privacy ...

following attributes: Experience working with large-scale transactional, behavioural, or operational datasets Experience with cloud platforms such as Azure, AWS, or GCP Knowledge of MLOps, model monitoring, and deployment frameworks Exposure to Generative AI, Large Language Models (LLMs), or modern AI workflows Strong commercial awareness and the ability to translate ...

experience in one or more object-oriented languages (e.g. Python, Go, Java, C++). Advanced SQL knowledge. Experience with modern ML engineering practices including MLOps, model lifecycle management, CI/CD, and monitoring. Knowledge of experimental design, analysis, and scientific methodology. Customer-centric and pragmatic mentality with a focus ...

scalable applications. Experience making architecture and technology decisions within enterprise organisations. Strong knowledge of modern AI engineering practices, including LLMs, RAG, agentic workflows, and MLOps concepts. Experience working with cloud platforms, ideally AWS. Ability to balance innovation with risk, governance, and operational requirements. Strong stakeholder management and communication skills. Experience ...

field Experience across a spectrum of libraries, frameworks and services : such as ML libraries e.g. TensorFlow or PyTorch model servers such as AWS Bedrock MLOps such as ML Flow or Sagemaker agent/AI orchestration such as LangGraph or MCP libraries Strong programming skills, especially in Python (other languages like ...

Engineer and ML Engineer through to Senior, Lead, Applied Scientist and Research Engineer positions—for upcoming roles across applied machine learning, generative AI, MLOps and model productionisation. These are hands-on engineering roles where you'll build, train, ship and operate the models that drive real product behaviour and real … models at scale LLM and generative AI tooling exposure (LangChain, LlamaIndex, vector databases such as Pinecone, Weaviate or pgvector, RAG patterns, fine-tuning workflows) MLOps platforms (MLflow, Kubeflow, Vertex AI, SageMaker, Databricks or equivalent) Cloud experience (AWS, GCP, Azure), containerisation and basic data engineering/SQL Familiarity with experimentation ...

into enterprise platforms Collaborating closely with business stakeholders to translate requirements into scalable AI solutions Driving best practice across AI governance, model monitoring and MLOps Evaluating emerging AI technologies and identifying opportunities for innovation Skills & Experience We're looking for candidates with experience across several of the following: Commercial experience … learn Experience with Generative AI technologies including LLMs, LangChain or LlamaIndex Hands-on experience building RAG solutions and working with vector databases Experience with MLOps, CI/CD and containerisation Strong SQL skills and experience working with large datasets Cloud experience across Azure, AWS or GCP Excellent communication skills with ...

Experience: Strong commercial understanding of the modern AI ecosystem, including: Generative AI Large Language Models (LLMs) Prompt Engineering Retrieval-Augmented Generation (RAG) Vector Databases MLOps Experience leading the implementation of enterprise-scale AI solutions and being accountable for delivery outcomes. Knowledge of cloud platforms such as AWS, Azure ...

Experience: Strong commercial understanding of the modern AI ecosystem, including: Generative AI Large Language Models (LLMs) Prompt Engineering Retrieval-Augmented Generation (RAG) Vector Databases MLOps Experience leading the implementation of enterprise-scale AI solutions and being accountable for delivery outcomes. Knowledge of cloud platforms such as AWS, Azure ...

modern AI technologies and concepts, including: Generative AI Large Language Models (LLMs) Prompt Engineering Retrieval-Augmented Generation (RAG) Vector Databases AI Solution Architecture MLOps Principles AI Governance and Risk Considerations Candidates are not expected to be hands-on developers but should be capable of guiding technical teams and contributing ...

approaches Agent-based architectures A practical understanding of how to apply LLMs to real-world use cases Tools & Engineering Experience with model deployment and MLOps tools (e.g. Databricks, MLflow) Familiarity with version control (Git) and collaborative development practices Awareness of best practices in code quality and reproducibility Communication & Consulting Ability ...

environments Nice to Have Hands-on experience building LLM or agentic AI applications Experience with frameworks such as LangGraph, RAG or MCP Understanding of MLOps and AI deployment practices Experience testing and evaluating non-deterministic AI systems Infrastructure as Code and cloud platform experience, ideally AWS Why Join? Competitive salary ...

degree or equivalent experience. What Will Make You Stand Out: Experience delivering AI and automation initiatives that drive measurable business value. Knowledge of MLOps, AI governance and responsible AI frameworks. Experience building modern, scalable data platforms and architectures. A track record of leading organisational change through data and digital transformation. ...

thermal, radar) Model optimisation/deployment tooling (ONNX, TensorRT) Edge deployment experience (e.g., NVIDIA Jetson or other resource-constrained devices) Familiarity with MLOps tooling (e.g., DVC, MLflow) Relevant open-source contributions in computer vision Interested? If this sounds like you, apply with your CV or get in touch to discuss ...

Engineer (LLMs/Azure/MLOps) Global Professional Services Firm Location: London Working: Hybrid (3 days office/2 days remote) Salary: Up to £85k + benefits The Role An exciting opportunity to join a global professional services organisation investing heavily in AI, with a focus on delivering enterprise-grade … services using retrieval, tool-calling and orchestration patterns Deliver end-to-end AI/ML pipelines covering experimentation, deployment, monitoring and optimisation Apply MLOps and LLMOps best practices across model lifecycle management, governance and production monitoring Work closely with Data Engineers, Platform Engineers and business stakeholders to deliver enterprise ...

CODAS project and related initiatives at AND-E Lab. Ideal candidates have 5+ years in ML/AI, strong Python, cloud/MLOps experience, and a collaborative mindset to work with partners and stakeholders across experiments and #J-18808-Ljbffr ...

data architecture decisions across portfolios. Data, AI Platforms & Technical Leadership Define and evolve enterprise data architecture and AI platform strategy (data pipelines, MLOps, model lifecycle management). Drive adoption of cloud‐native, scalable AI architectures and modern data ecosystems. Lead the integration and architectural design of AI capabilities within core ...

engineering standards, and drive high-impact ML products from research to deployment. The role requires deep ML expertise, strong software engineering, and experience in MLOps, distributed systems, and scalable APIs. #J-18808-Ljbffr ...

experimentation through to productised, maintainable solutions that deliver measurable value. Drive engineering excellence across ML systems, including CI/CD, testing, observability, reliability, and MLOps guidelines. Define technical standards, patterns, and protocols for ML engineering and applied ML science across teams. Lead complex, multi-team technical initiatives and influence organisational … systems, scalable architectures, and API design. Advanced programming experience in languages such as Python, Go, Java, or C++. Advanced SQL knowledge. Strong experience with MLOps, production ML systems, model lifecycle management, and monitoring. Experience working with large-scale data systems and distributed computing frameworks (e.g. Spark, Hadoop). Knowledge ...

Senior AI Transformation Leader - London (Hybrid) - Up to £115,000 An established organisation delivering AI and digital transformation solutions is seeking a Senior AI Transformation Leader to support the growth of its Telecoms, Media & Technology ...

GenAI capabilities across our business, delivering secure and reliable AI solutions for animals and customers. The role requires deep expertise in NLP, LLMs, and MLOps, plus modern cloud architectures. Hybrid work is available with three days per week in the Hook office, UK. #J-18808-Ljbffr ...

Job Title: DevOps Engineer Location: Durrington, hybrid Contract Type: Permanent Hours: 37 per week Salary: from £50k About the role We're looking for a forward-thinking DevOps Engineer to play a key role in ...

seeks a Head of AI Engineering to lead the design, deployment and governance of enterprise AI capabilities, including ML platforms, generative AI and MLOps tooling. You will establish AI engineering standards, guardrails and best practices across the model lifecycle, ensuring scalable, secure and compliant solutions delivering measurable business value. ...

design, deployment, integration and continuous improvement of enterprise AI and machine learning capabilities, including ML platforms, generative AI, LLM services, feature pipelines and MLOps tooling. The role is responsible for establishing AI engineering standards, governance and best practices across the model lifecycle, ensuring solutions are scalable, secure, compliant and deliver … measurable business value. About the role Lead AI engineering, MLOps and platform teams, driving high-quality delivery and operational excellence. Define and maintain engineering standards, AI guardrails and Responsible AI practices. Oversee model development, deployment, monitoring, evaluation and continuous improvement processes. Drive preventative controls, automation, observability and root cause analysis ...

design, deployment, integration and continuous improvement of enterprise AI and machine learning capabilities, including ML platforms, generative AI, LLM services, feature pipelines and MLOps tooling. The role is responsible for establishing AI engineering standards, governance and best practices across the model lifecycle, ensuring solutions are scalable, secure, compliant and deliver … measurable business value. About the role Lead AI engineering, MLOps and platform teams, driving high‐quality delivery and operational excellence. Define and maintain engineering standards, AI guardrails and Responsible AI practices. Oversee model development, deployment, monitoring, evaluation and continuous improvement processes. Drive preventative controls, automation, observability and root‐cause analysis ...