1 to 25 of 49 MLOps Jobs in the South East

Python Docker Preferred Skills: Experience with specialized AI domains such as conversational AI, sentiment analysis, or recommendation systems. Knowledge of model interpretability techniques and responsible AI practices. Familiarity with MLOps pipelines for continuous integration and deployment of AI models. Experience with API development and integration for deploying AI services. Prior experience in working with proprietary or sensitive data. Benefits What More ❯

real-world tradeoffs. Fluency in Python and commonly used ML libraries (e.g. pandas, scikit-learn; experience with PyTorch or TensorFlow is a plus). Experience with model lifecycle management (MLOps), including monitoring, retraining, and model versioning. Ability to work across data infrastructure, from SQL to large-scale distributed data tools (Spark, etc.). Strong written and verbal communication skills, especially More ❯

Computer Science, Machine Learning) Strong programming skills in Python (production-level) and SQL; confident with modern ML/AI libraries such as scikit-learn, TensorFlow, or PyTorch Familiarity with MLOps frameworks, model deployment, and cloud-based platforms (Databricks, AWS, Azure) Strong experience with data visualisation tools and techniques; able to turn complex results into accessible insights Excellent communicator; able to More ❯

model endpoints, pipelines, embeddings, vector search or equivalent cloud-native AI/ML platforms e.g. AWS SageMaker, Azure ML and agent orchestration frameworks e.g. LangChain, LangGraph. ·Solid understanding of MLOps CI/CD, IaC Terraform, experiment tracking, model registry, and monitoring. ·Proven experience deploying and operating ML systems in production batch and real-time. ·Familiarity with RAG architectures, prompt engineering More ❯

Engineer - AI for Advanced Materials - Oxford/Remote (UK) (Tech stack: Python, PyTorch, TensorFlow, Scikit-learn, MLflow, Airflow, Docker, Kubernetes, AWS, Azure, GCP, Pandas, NumPy, SciPy, CI/CD, MLOps, Data Visualization, Bayesian Modelling, Probabilistic Programming, Terraform) We're looking for a Machine Learning Engineer to join a rapidly scaling deep-tech company that's reinventing how the world designs More ❯

AI tools to amplify your productivity. Proven experience designing, deploying, and managing scalable AI solutions on at least one major cloud platform (AWS, GCP, Azure). Solid understanding of MLOps principles and experience with relevant tools (e.g., Docker, Kubernetes, MLflow) for building robust, production-ready systems. The Ideal Candidate Profile: This role is a perfect fit if you... Are at More ❯

Engineer - AI for Advanced Materials - Oxford/Remote (UK) (Tech stack: Python, PyTorch, TensorFlow, Scikit-learn, MLflow, Airflow, Docker, Kubernetes, AWS, Azure, GCP, Pandas, NumPy, SciPy, CI/CD, MLOps, Data Visualization, Bayesian Modelling, Probabilistic Programming, Terraform) We're looking for a Machine Learning Engineer to join a rapidly scaling deep-tech company that's reinventing how the world designs More ❯

with Front end Frameworks such as Angular, for integration of AI-powered applications Experience with graph databases and knowledge graphs (Neo4j) for knowledge graphs and tool routing. Cloud deployment & MLOps Production deployments on Azure (AKS/ACI/Functions), CI/CD, and Infrastructure as Code (Bicep/Terraform). Data & Information Management Experience with relational/semi-structured database More ❯

CI/CD pipelines, and GitHub Actions. Knowledge of containerization (tools ie Docker) and orchestration (ie Kubernetes on Azure). Good awareness of Data & AI - understanding of ML lifecycle, MLOps, and Responsible AI. Strong problem-solving and analytical skills. Excellent communication and stakeholder management skills. Nice to have: Familiarity with LLM fine-tuning. Strong academic background in IT/Computer More ❯

pragmatic mindset - able to balance innovation with execution. Nice-to-haves: Experience leading platform or DevOps product teams in life sciences, research, or AI-driven organisations . Familiarity with MLOps, GenAI, and large-scale compute orchestration . Background in computer science, engineering, or a related technical discipline. Package & Benefits: Base salary up to £145,000 . Car allowance , 18% bonus More ❯

managing business-critical machine learning models using Azure ML in Production environments. Experience in data wrangling using Python, SQL and ADF. Experience in CI/CD and DevOps/MLOps and version control. Familiarity with data visualization and reporting tools, ideally PowerBI. Good written and verbal communication and interpersonal skills. Ability to convey technical concepts to non-technical stakeholders. Experience More ❯

for security clearance. Desirable Skills, but not essential: Proficiency in Arcade scripting across ESRI runtime environments. Familiarity with the Microsoft Azure cloud platform, including data and AI services, and MLOps Knowledge of geospatial data warehousing and analytics. Familiarity with leading AI/ML platforms and libraries Experience building and scheduling ETL workflows. Knowledge of spatial data management and geospatial queries. More ❯

and analyse large, complex datasets. Strong understanding of data ethics, governance, and responsible AI principles. Must have SC Clearance and 5 years' continuous UK residency . Desirable: Experience with MLOps or deploying ML models into production. Familiarity with data engineering workflows and pipelines. Knowledge of working in a secure or public sector environment. If you believe your experience is in More ❯

LLM systems, balancing cost, latency, and performance. Is familiar with observability frameworks such as Langfuse, LangSmith, or OpenTelemetry. Has worked with knowledge graphs and graph databases. Is comfortable with MLOps workflows — Docker, Kubernetes, CI/CD, and cloud environments. Communicates clearly, collaborates actively, and moves fast without breaking quality. Is curious, pragmatic, and resourceful — figuring things out even when the More ❯

and unsupervised learning Understanding of Transformer based models Experience developing AI agents Strong Python and SQL skills Experience with Cloud ML tools and version control (e.g. git) Experience with MLOps Collaborative, proactive, logical, methodical, and attentive to detail Excellent communication skills (verbal and written) Collaborate with clients to understand their business problems and design technical solutions using machine learning models More ❯

Qualifications: • 6+ years of experience in applied data science, machine learning, or analytics leadership. • Strong understanding of model lifecycle management, experimentation frameworks, and data science governance. • Familiarity with MLOps concepts and tooling (e.g., MLflow, Kubeflow, Vertex AI, SageMaker, Azure ML). • Hands-on experience with data science tools and languages such as Python, R, SQL, and relevant frameworks (e.g., scikit More ❯

agile delivery approach, consistently documenting designs and results for leadership. Experience and Expertise Required We are looking for hands-on experience in the following core areas: Model Lifecycle Management (MLOps): Proven experience in implementing robust AI/ML pipelines for model training, validation, and deployment (e.g., using MLflow, Vertex AI, or Azure ML). Expertise in managing model evaluation, drift More ❯

MLOps Engineer Outside IR35 - 500-600 Per Day Ideally, 1 day per week/fortnight in the office, flexibility for remote work for the right candidate. A market-leading global e-commerce client is urgently seeking a Senior MLOps Lead to establish and drive operational excellence within their largest, most established data function (60+ engineers). This is a mission … critical role focused on scaling their core on-site advertising platform from daily batch processing to real-time capability. This role suits a hands-on MLOps expert who is capable of implementing new standards, automating deployment lifecycles, and mentoring a large engineering team on best practices. What you'll be doing: MLOps Strategy & Implementation: Design and deploy end-to-end … MLOps processes, focusing heavily on governance, reproducibility, and automation. Real-Time Pipeline Build: Architect and implement solutions to transition high-volume model serving (10M+ customers, 1.2M+ product variants) to real-time performance. MLflow & Databricks Mastery: Lead the optimal integration and use of MLflow for model registry, experiment tracking, and deployment within the Databricks platform. DevOps for ML: Build and automate More ❯

Knowledge of prompt engineering, vector databases (Pinecone, ChromaDB, FAISS), and model fine-tuning Cloud deployment experience with Docker/Kubernetes Understanding of transformer architectures, AI/ML principles, and MLOps Experience developing REST APIs Desirable Exposure to Insurtech or Fintech Familiarity with insurance workflows or policy administration systems Why Join? Work on high-impact AI projects with mentorship from senior More ❯

Experience integrating AI models into production systems using GCP, AWS, or Azure. Familiarity with vector databases, embedding models, or retrieval-augmented generation (RAG). Knowledge of Docker, Airflow, or MLOps pipelines. Strong understanding of AI ethics, data privacy, and responsible model deployment. TO BE CONSIDERED... Please either apply online or email me directly at .By applying for this role, you More ❯

ChromaDB, FAISS) Proven team leadership and mentoring skills ; managing 3-5 engineers Cloud deployment experience with Docker/Kubernetes Deep understanding of transformer architectures, AI/ML principles, and MLOps Experience developing REST APIs and Agentic AI frameworks (LangGraph, Crew AI) Desirable Experience in Insurtech or Fintech domains Knowledge of insurance workflows or policy administration systems Team leadership and client More ❯

ambitions for growth and scale • Create frameworks, infrastructure and systems to manage and govern Ki's data asset • Work with the broader Engineering community to develop our data and MLOps capability infrastructure Requirements • Strong experience in software engineering with proficiency in a language such as Python for API development, Data engineering and automation tasks • A background in working with storage More ❯

optimise systems that enable large-scale data processing, AI-driven analytics, and simulation workloads across. For example deploying Kubernetes and Slurm to enable real-time data analysis from instruments, MLOps, or scientific workflow managers. We will be hiring either at the regular or senior level, depending on the applicant's experience. At the regular level: This position requires technical expertise More ❯

Storage, Delta Lake etc Experience with Purview, Unity Catalog etc Experience with streaming Data in Kakfa/Event Hubs/Stream Analytics etc Experience working in the Databricks ecosystem MLOps This is an exciting contract opportunity working at the cutting edge of technologies. To be considered, please click apply More ❯

clients Defence Information Advantage team to drive best practice in end-to-end SDLC and deployment automation. The Defence domain represents many unique and cutting-edge challenges in DevSecOps, MLOps and utilisation of cloud for example updating deployed equipment in a safe and secure manner. My client seek an innovative thought-leader who can set technical direction and coach others More ❯