1 to 25 of 36 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 ...

build scalable pipelines, develop and evaluate models and help deliver cloud-based AI solutions across Azure, AWS or GCP. You'll also contribute to MLOps practices, support production deployments and work with both structured and unstructured datasets. You'll collaborate closely with engineers, data professionals and business stakeholders to turn ...

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 ...

data‐driven systems in production or near‐production settings. Experience with generative AI systems, LLM‐enabled applications or evaluation pipelines. Experience with cloud platforms, MLOps tooling, model serving or platform engineering. Advanced use of Linux, Docker, version control, CI/CD and service‐oriented deployment practices. Experience working across ...

directly with senior business stakeholders and translate requirements into build Mentor engineers and squad tech leads to understand and deliver on the architecture Shape MLOps routes to live, CI/CD and generative AI abstraction layers Model the cost, feasibility and risk of different LLMs and implementation approaches THE STACK ...

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 ...

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 ...

technical delivery while also leading multiple projects or workstreams in parallel. Desirable • Experience establishing or embedding best practice in ML engineering, software delivery or MLOps within a team or function. • Experience in regulated, enterprise or operationally complex environments. • Experience promoting reuse through shared frameworks, tooling, templates or engineering standards. • Experience ...

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 ...

stakeholders and helping shape technology and investment decisions. Mentoring architects and engineers, helping to develop capability across the wider team. Driving best practice across MLOps, AIOps, CI/CD and modern cloud engineering approaches. What we're looking for Extensive experience designing and delivering modern data platforms, analytics solutions ...

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 ...

scalable ML pipelines and engineering frameworks AI model training, evaluation, optimisation and deployment Cloud-native AI and data platforms across Azure, AWS and GCP MLOps tools and best practices for deployment, monitoring and governance Structured and unstructured datasets Generative AI and advanced analytics solutions Designing and maintaining scalable data pipelines … cloud environments Knowledge of data engineering principles including ETL processes, data pipelines and automated workflows Experience working with structured and unstructured data Understanding of MLOps tooling and best practices Knowledge of data governance, security and compliance principles within AI and data environments Strong analytical and problem-solving ability Experience working ...

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 ...

Engineering to lead the design, deployment, and governance of enterprise AI capabilities. You will oversee ML platforms, generative AI, LLM services, feature pipelines and MLOps tooling to deliver measurable business value. The role requires significant leadership in AI engineering, deep ML and cloud experience, and a track record of driving ...

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 ...

currently available globally. The Role We are building custom AI hardware and the full-stack software ecosystem to run it. As our first dedicated MLOps Engineer, you will own the infrastructure that takes models from research to silicon-validated production — designing, building, and operating the pipelines, tooling, and platforms that ...

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 ...

ability to drive adoption, influence behaviours, and deliver outcomes at pace Exposure to technologies, modern cloud platforms, customer data platforms, enterprise BI tooling, or MLOps governance environments would be highly advantageous. Why Join? This is an opportunity to define and shape governance strategy during a significant period of enterprise transformation. ...