1 to 25 of 44 MLOps Jobs in the South East

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 ❯

the ability to balance research innovation and commercial value delivery. Comfortable operating across cloud environments (GCP, Azure, AWS) and deploying AI/ML systems at scale. Strong knowledge of MLOps practices and infrastructure including CI/CD, model versioning, orchestration, and monitoring. A collaborative and inspiring leader who brings out the best in technical teams and communicates complex concepts to More ❯

working in B2C, e-commerce, subscription, retail, or fintech industries is a strong advantage Nice to Have Experience with NLP for customer feedback or support ticket analysis Exposure to MLOps for model deployment and monitoring Familiarity with tools like Amplitude, Mixpanel, or Google Analytics Why Join Us? Shape data-driven strategy for how we understand and engage with our customers More ❯

or Azure SQL Database is beneficial. Experience in designing and maintaining robust CI/CD pipelines, applying Infrastructure as Code (IaC) principles with exposure to Terraform, and incorporating Azure MLOps practices for seamless deployment, versioning, and traceability of AI models and data Knowledge of DevSecOps principles and data governance best practices to secure AI infrastructure and ensure compliance. The ability More ❯

or Azure SQL Database is beneficial. Experience in designing and maintaining robust CI/CD pipelines, applying Infrastructure as Code (IaC) principles with exposure to Terraform, and incorporating Azure MLOps practices for seamless deployment, versioning, and traceability of AI models and data Knowledge of DevSecOps principles and data governance best practices to secure AI infrastructure and ensure compliance. The ability More ❯

project governance, lifecycle management, and ROI tracking. Vendor Management: Evaluate and manage relationships with external AI platform vendors and partners. Stay Ahead: Keep ClearCourse at the forefront of GenAI, MLOps, RPA, and regulatory trends. What You’ll Bring: Essential: Proven experience leading AI, ML, or automation initiatives in a digital or tech-driven organisation Strong knowledge of GenAI, NLP, LLMs More ❯

with distributed systems (Spark, Databricks, etc.). Familiarity with semi-structured and unstructured data formats. Knowledge of machine learning frameworks and how to operationalise models in production. Understanding of MLOps and AI model lifecycle management is a plus. Benefits: 25 Days holiday (excluding bank holidays) – increasing by a day every 2 years. Flexible working options – hybrid. Mental health and wellbeing More ❯

Demonstrated ability to build relationships with stakeholders and subject matter experts. Familiarity with high computer cloud-based platforms and services, in particular AWS. Familiarity with code version control and MLOps deployment approaches. Ability to understand healthcare challenges and adapt accordingly to the AI solutions. Cross-functions high adaptability to meet cross organization goals. Required/Preferred Education Advanced degree (MS More ❯

Demonstrated ability to build relationships with stakeholders and subject matter experts. Familiarity with high computer cloud-based platforms and services, in particular AWS. Familiarity with code version control and MLOps deployment approaches. Ability to understand healthcare challenges and adapt accordingly to the AI solutions. Cross-functions high adaptability to meet cross organization goals. Required/Preferred Education Advanced degree (MS More ❯

Demonstrated ability to build relationships with stakeholders and subject matter experts. Familiarity with high computer cloud-based platforms and services, in particular AWS. Familiarity with code version control and MLOps deployment approaches. Ability to understand healthcare challenges and adapt accordingly to the AI solutions. Cross-functions high adaptability to meet cross organization goals. Required/Preferred Education Advanced degree (MS More ❯

Demonstrated ability to build relationships with stakeholders and subject matter experts. Familiarity with high computer cloud-based platforms and services, in particular AWS. Familiarity with code version control and MLOps deployment approaches. Ability to understand healthcare challenges and adapt accordingly to the AI solutions. Cross-functions high adaptability to meet cross organization goals. Required/Preferred Education Advanced degree (MS More ❯

and ownership Nice to Have Experience with Data Mesh or Data Lake architectures Familiarity with Kubernetes , Docker, and real-time streaming (Kafka, Kinesis) Exposure to ML engineering pipelines or MLOps Interested? If you're ready to lead a team at the forefront of data innovation in a mission-driven scale-up, apply today or reach out for a confidential chat. More ❯

CI/CD pipelines. Experience writing clean, efficient, and modular code suitable for production environments. Desirable Skills or Knowledge Familiarity with AMO physics or quantum machine learning. Experience with MLOps best practices. Knowledge of systems like Apache Kafka, MQTT or real-time data pipelines. Experience with cloud platforms (AWS, Azure, GCP). Deep expertise in time series modelling techniques (e.g. More ❯

pipelines. Comfortable in fast-paced, agile delivery environments. DESIRABLE SKILLS Experience building or integrating feature stores . Familiarity with Unity Catalog , Databricks SQL , and cost optimisation . Exposure to MLOps practices and production-grade model lifecycle management. Prior experience in financial services or other regulated sectors. HOW TO APPLY Please register your interest by sending your CV via the apply More ❯

/sub patterns for asynchronous processing Integrate with third-party data sources like Snowflake and Databricks Create robust error handling, monitoring, and retry systems for high availability Collaborate with MLOps and DevOps teams to optimise architecture and deployment pipelines What You'll Bring 5+ years in backend or fullstack engineering roles Strong Python skills, ideally with FastAPI and the ASGI More ❯

rest/in-transit), token management Identity integration (Google Identity, Azure Entra ID) Data Security & Governance RAG pipelines with data classification, masking, and DLP GDPR and data residency compliance MLOps & DevSecOps GitHub Actions, CI/CD security testing, model drift detection, audit logging Lab Environment Tooling Infrastructure-as-Code (IaC): Terraform, Pulumi Containerization & Orchestration: Docker, Kubernetes (GKE/AKS) Networking More ❯

environments Strong technical background in data engineering, analytics, or science Experience working with Finance and Risk teams Familiar with AWS and modern data/AI stacks (SQL, Python, MLflow, MLOps) Strategic mindset with ability to scale teams and influence at C-level More ❯

Experience:* Python, including APIs, data structures, and async processing* Databricks/Microsoft Fabric* Cloud, preferably Azure (Data Lake, Functions, App Services)* Containerisation with Docker and CI/CD pipelines* MLOps tooling (MLFlow, Git-based versioning, environment tracking)Desirable Skills & Interests* LangChain, Langflow, or similar frameworks for building AI agents* LLMs or intelligent automation workflows* High-availability, scalable systems (microservices, event More ❯

in data engineering, analytics, or data science. Experience with modern data stacks (e.g., SQL, dbt, Airflow, Snowflake, Looker/Power BI) and AI/ML tooling (e.g., Python, MLflow, MLOps). A track record of building and managing high-performing data teams. Strategic thinking and ability to influence senior stakeholders, comfortable in dual-regulated environments. HOW TO APPLY: Please send More ❯

libraries like Scikit-learn, TensorFlow, PyTorch, or Hugging Face Solid understanding of machine learning fundamentals and performance evaluation techniques Experience working in cloud platforms (AWS, GCP, or Azure) with MLOps tools (e.g. MLflow, SageMaker, Vertex AI) Comfortable working independently and delivering high-quality work to tight timelines Experience working in fast-paced environments or scale-up settings Company Market leading More ❯

with large-scale data and cloud-based environments. Background in consulting or client-facing project delivery. Familiarity with sectors such as manufacturing, energy, healthcare, or public sector. Understanding of MLOps and data science solution lifecycle. Why Join? Work on groundbreaking AI projects with real-world applications.? Collaborate with a team that values innovation, creativity, and measurable impact.? Opportunity to influence More ❯

reviews, observability and performance profiling. - ️ Experience using multi modal AI (document layout, vision language models, building agents). - Experience building quick prototypes (e.g. Streamlit, Dash) to validate ideas rapidly. - MLOps tooling (e.g. MLflow) and model monitoring experience. - Strong communication skills and action oriented. Nice to Have - ️ Hands on with Azure services (Storage, Container Registry, OpenAI Services). - Demonstrated experience building More ❯

you will design and develop scalable, cloud-based backend systems using multiple programming languages. You'll contribute to QA, automation, and infrastructure management while supporting DevOps and AI/MLOps practices. Collaboration on architecture, environment lifecycle, and regulatory compliance is key. As Full Stack Software Engineer, you will have to : Develop scalable software systems for back-end that will be More ❯

and AI-driven recommendations. Technical Leadership: Experience in AI product scalability, DevOps, and cloud architecture. Backend & Data Skills: Proficiency in Python, Node.js, PostgreSQL, MongoDB, API security. AI Model Deployment: MLOps, scaling, data engineering, and AI ethics awareness. Strategic Mindset: Ability to align technology with business objectives and cost efficiency. Security & Compliance Knowledge: GDPR, API authentication, and observability best practices. Big More ❯

with internal policies and external regulations. Provide technical input on risk mitigation strategies and onboarding documentation. Security & DevSecOps Integration Integrate AI security controls into CI/CD pipelines and MLOps workflows. Use tools such as Azure Key Vault, Microsoft Entra ID, and GitHub Actions for secure deployment and access management. Monitor AI systems using Azure Monitor, Log Analytics, and Application More ❯