17 of 17 Permanent MLOps Jobs in the South West

Databricks, with familiarity with Azure DevOps tools such as Boards, Repos, and Pipelines. Strong knowledge of explainable AI methods and best practices in MLOps and LLMOps. Advanced programming skills in Python and SQL, with a solid understanding of data visualisation techniques. Experience working in agile and cross‐functional teams, with ...

internal stakeholders. Excellent coding practice using Python and associated ML packages (HuggingFace, TensorFlow, PyTorch), following best practices. Experience developing robust ML pipelines with MLOps tools for model training, monitoring and deployment. Experience of appropriate version control and environment management (such as venvs or Docker). Experience of writing technical project ...

Learning Applied machine learning, computer vision, object detection, image classification, model development and evaluation, data annotation and curation, training pipelines, performance optimisation, edge ML, MLOps, deployment and maintenance of ML solutions, Python development, Python-based ML frameworks, PyTorch, TensorFlow, sklearn, OpenCV, NumPy, feature development, code review, pytest, linting, bug fixing ...

Computer Science, Machine Learning, or a related technical discipline. Bonus Skills/Experience Public writing, talks, or threads about building with AI. MLOps and model serving experience (BentoML, MLflow, Vertex AI, SageMaker). Streaming and batch ingestion pipelines (Spark, Airflow, Beam, Glue). Healthcare or life sciences domain exposure. ...

areas, and encourage anyone with the experience listed above to apply. We would particularly like to encourage applicants with experience in the following areas: MLOps: including tools like LangChain for application development and managing the ML lifecycle Data Engineering: including platforms like Databricks for data processing and analytics DevSecOps & DevOps ...

flows and lifecycle management from ingestion to consumption. Enable Advanced Analytics & ML Partner with the data science and software development teams to establish strong MLOps and DataOps practices. Support geospatial and large‐scale analytical workloads. Ensure data is discoverable, reusable, and fit for experimentation and production ML. Governance & Client Engagement ...

development and deployment of ML and AI models to prove and deliver tangible commercial value. Act as a technical bridge between Data Science and MLOps to embed best practice deployment techniques across the team. Take hands-on ownership of big projects, working with a large, event-based data set. Research ...

performance. Bachelor/Master's/PhD or equivalent experience in Computer Science, Maths, Machine Learning, Data Science, or related field. Desirable Experience with MLOps pipelines, model deployment, or production ML systems. Familiarity with performance analysis, profiling tools, or numerical accuracy validation. Exposure to distributed training or inference systems. Experience ...

government, and national security. Working alongside Data Scientists, Software Engineers, and AI specialists, you'll take projects from experimentation through to production, leveraging modern MLOps and LLMOps practices on AWS to build scalable, secure AI systems. What You'll Be Doing Design, develop, and deploy machine learning models for traditional … frameworks. Lead experimentation cycles, defining hypotheses, running evaluations, and iterating on models before production deployment. Develop robust ML pipelines using AWS services and MLOps tooling. Implement experiment tracking, model versioning, monitoring, and reproducibility best practices. Build feature engineering pipelines and contribute to feature store development. Monitor production models, optimise performance ...

Mathematics or Data Science Commercial experience delivering ML solutions Good Python skills In depth knowledge of AI/ML Experience developing ML pipelines with MLOps Cloud & Linux experience would be beneficial but not essential Security Clearance Eligibility for Security Clearance is required for this role Apply To apply, follow ...

across both software development and infrastructure. You’ll enjoy solving complex technical challenges, working in dynamic environments, and collaborating closely with Data Scientists and MLOps teams. A pragmatic, adaptable mindset is key, along with a passion for building scalable, secure systems that enable data‐driven outcomes. You should also … data management initiatives. Develop, test and deploy new platform capabilities. Troubleshoot and enhance existing analytics services. Provide hands‐on support to Data Scientists and MLOps teams. Tackle complex engineering challenges across a varied tech stack. Requirements: Strong experience with Python. Experience with Kubernetes and Docker. Understanding of CI/ ...

data solutions. This role will see you building and maintaining scalable platform capabilities, troubleshooting existing services, and providing technical support to Data Scientists and MLOps teams working across a range of technologies. Key Responsibilities of the Analytics Platform Engineer Design, build and maintain scalable analytics platforms and cloud-based infrastructure. … platform capabilities while enhancing and maintaining existing analytics solutions. Troubleshoot technical issues, implement improvements and optimise platform performance. Collaborate closely with Data Scientists, MLOps Engineers and other technical teams to deliver reliable, secure and high-performing solutions. Adapt to a varied workload, solving complex engineering challenges across a range ...

will develop, test, deploy, and enhance platform capabilities, resolve issues within existing analytics services, and provide hands-on engineering support to Data Scientists and MLOps teams working across a range of technologies. You will operate in a dynamic environment with varied and sometimes unfamiliar technical challenges, requiring strong problem-solving … lifecycle. Design, develop, test, and deployment of a new analytic hosting platform. Bugfixes and feature requests for existing mission facing analytics. Engineering support to MLOps engineers and Data Scientists. These are unpredictable and highly variable requirements, potentially using unfamiliar technology, so a flexible style is required. In this role ...

cloud migration, analytics platforms, and data-driven solutions. This is an excellent opportunity to work on mission-critical systems, supporting Data Scientists and MLOps teams while helping to design, build and evolve a scalable analytics hosting platform. Start Date: ASAP Job Type: Contract or Permanent Duration: 2 Months Initially … across the full software development life cycle, delivering platform enhancements, resolving issues within existing analytics services, and providing engineering support to Data Scientists and MLOps teams. Key Responsibilities: Designing, developing, testing and deploying a new analytics hosting platform Supporting cloud migration and data management initiatives Building scalable, secure, and resilient ...

scalability. • Design for Resilience: Own multi-AZ/multi-region strategy, disaster recovery, and chaos-engineering practices for mission-critical AIOps platforms. • Enable AIOps & MLOps: Partner with data science and MLOps teams to embed anomaly detection and predictive-maintenance models into production system design. • Own Non-Functional Requirements: Balance latency ...

Responsibilities Lead the Machine Learning Engineering team in the safe and effective operation of AI/ML products and services in production, maximising the value and adoption of data‐driven decision making across the business. ...

NLP PEOPLE is seeking a leader for the Machine Learning Engineering team in Bristol. This hybrid role involves guiding the operation of AI/ML services, ensuring robust governance and stakeholder engagement. The ideal candidate ...