1 to 25 of 31 Permanent MLOps Jobs in the South West

PyTorch, or Java-based ML libraries) Strong problem-solving and communication skills Preferred Qualifications Experience with NLP, computer vision, or recommendation systems Familiarity with MLOps practices and model lifecycle management Knowledge of event-driven architectures (Kafka or similar) Experience with DevOps tooling and infrastructure as code Exposure to Generative ...

model capabilities and AI product stacks. Machine Learning Engineering: End-to-end ML lifecycle expertise: feature engineering, model training, evaluation and production deployment, including MLOps, monitoring and drift detection. Practical knowledge of ML frameworks (e.g. scikit-learn, PyTorch, XGBoost) and cloud-native ML services (SageMaker, Azure ML, Vertex AI). ...

optimization and ML theory. Familiarity with popular deep learning architectures (transformers, CNN, autoencoders etc.) Specialism or well-researched interest in NLP Broad knowledge of MLOps tooling - for versioning, reproducibility, observability etc. Experience monitoring, maintaining, enhancing existing models over an extended time period Extensive experience with pytorch and related data science ...

engineering, fine-tuning, RAG) · LangChain/LangGraph · Agent frameworks · Vector databases · FastAPI/APIs · AWS/Azure/GCP/Databricks · CI/CD, MLOps/LLMOps What you'll bring: - Strong software or data engineering foundations - Experience delivering AI or AI-enabled solutions - Exposure to cloud and modern data ...

Computer Science, Data Science, Machine Learning, or related field. Proven experience building and deploying AI applications in large-scale production environments. Experience with MLOps practices and tools for managing the machine learning lifecycle. Proficiency in Python and machine learning frameworks such as PyTorch, TensorFlow, and scikit-learn. Strong understanding ...

professional accreditation such as CEng. Experience with graph databases and advanced query languages (e.g., Neo4j, Gremlin). Knowledge of machine learning data pipelines and MLOps practices. Familiarity with data virtualisation and data mesh concepts. Hands-on experience with containerisation and orchestration (Docker, Kubernetes, Red Hat OpenShift and Ceph). Exposure ...

code tools, and containerization/orchestration tools (Docker, Kubernetes) to operate, support, and secure mission-critical applications Preferred qualifications, capabilities and skills Experience with MLOps tools and platforms such as MLflow, Amazon SageMaker, Google VertexAI, Databricks, BentoML, KServe, and Kubeflow Exposure to cloud-native microservices architecture Familiarity with advanced ...

members and providing technical leadership. Desirable Skills & Experience MSc or PhD with a strong research background. Experience developing and maintaining robust ML pipelines and MLOps practices. Familiarity with version control, containerisation and environment management tools, including Git and Docker. Experience working within Linux environments and using command-line tooling. Experience ...

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 ...

projects and deployments of AI systems. Provide guidance and mentoring to other team members on best practices in AI engineering. Use best practices (e.g., MLOps, AIOps) to improve products/services and processes related to AI. Optimise existing model serving and data pipelines to meet changing performance and security requirements. ...

model development and deployment of AI/ML applications in a production environment, with expertise in deploying models on AWS platforms. Deep familiarity with MLOps practices, covering the full cycle from design, experimentation, deployment, to monitoring and maintenance of machine learning models. Expertise in machine learning frameworks such as TensorFlow ...

Extension or Model Context Protocol (MCP) Experience with AWS and infrastructure-as-code tools such as Terraform. Experience in multi-agent orchestration. Familiarity with MLOps practices, including CI/CD for ML, model monitoring, automated deployment, and ML pipelines. Experience with agentic telemetry and evaluation services. ABOUT US J.P. Morgan ...

knowledge in the following: Python AWS Kubernetes Spark & distributed computing dbt Terraform Data Warehousing (we use Databricks but experience with other platforms is fine) MLOps implementation (desirable but not required) Airflow (or other orchestration tooling) Governance Why else you'll love it here Wondering what the salary for this role ...

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 ...

analytics Delivering production agentic systems, including RAG pipelines, vector search, and agentic frameworks such as LangChain, LlamaIndex, CrewAI, AutoGen, and n8n DevSecOps & DevOps MLOps Infrastructure as Code Building a truly diverse, equitable, and inclusive workplace is an ongoing journey, and we re excited to keep learning and growing together. ...

commercial environment Responsibilities include: Researching, advising, building, deploying and testing models from day one Work as a bridge between the data science and MLOps departments to continue bring understanding and best practice deployment techniques into the Data Science team Hands on big project ownership Everyday modelling and Data Science projects ...

Senior Software Developer Up to 80k Bristol, Hybrid Python, AWS and MLOps Benefits: 28 days holiday plus bank holidays Pension and matched contributions Private health care I am working with a new and exciting startup client looking for a Senior Software Developer using Python and AWS. They are looking … Python AWS API experience CI/CD SQL Communication Stakeholder engagement Leadership skills Infrastructure as code (Terraform or Ansible or other IAC) Desirable skills: MLOps practices AI practice This is an urgent vacancy, so if you would like to be considered then please apply quoting reference AR(phone number removed ...

system design. Desirable (AI & Data) Vector DBs: Milvus, Weaviate, Pinecone Data orchestration: Airflow, Prefect, Dagster Search: ElasticSearch/OpenSearch AI frameworks: LangChain, Hugging Face MLOps: MLflow, Kubeflow If you are looking for an opportunity to work on mission-critical systems at the forefront of UK National Security? Apply ...

intensive, AI/ML applications in an industrial setting - Substantial depth and breadth in system design, latency optimization, cloud infrastructure optimization, knowledge graphs, and MLOps - Deep understanding of CS fundamentals, computational complexity and algorithm design - Experience with building large-scale distributed systems in an agile environment and the ability ...

data collection and analysis to implementation and monitoring of solutions facilitate adoption of ML models through exceptional communication with stakeholders work in an established MLOps team at SB there is significant opportunity to deploy end to end and will be actively encouraged continually improve through active learning and development highlighting ...

impact. Experience with agentic telemetry, evaluation services, and orchestration of NLQ workflows. Demonstrated leadership working with engineers, data scientists, and AI practitioners. Familiarity with MLOps practices and AI pipelines. Hands-on experience building and maintaining user interfaces for NLQ and data exploration. ABOUT US J.P. Morgan is a global leader ...

quantitative discipline, e.g. Computer Science, Mathematics, Statistics Track record of developing, deploying business critical machine learning models Broad knowledge of MLOps tooling - for versioning, reproducibility, observability etc Experience monitoring, maintaining, enhancing existing models over an extended time period Specialism in NLP or Computer Vision Solid understanding of fundamentals of statistics ...

e.g., Gurobi, Google OR-Tools) to solve real world problems. Domain Knowledge: Prior experience in the Telecommunications, Utilities, Logistics, or Infrastructure sectors. xlzlddp Cloud & MLOps: Experience developing and deploying production-grade models in cloud environments. ...

designing and building APIs, distributed systems, and event-driven architectures in modern programming languages such as Python, TypeScript, Java, Go, or similar Familiarity with MLOps practices, model monitoring, observability, versioning, and automated deployment pipelines preferred Strong problem-solving skills with the ability to navigate ambiguity, experiment rapidly, and deliver impactful … cost efficiency while supporting increasing AI adoption and usage Implement engineering best practices around observability, monitoring, testing, security, and operational excellence Establish and champion MLOps practices including model lifecycle management, prompt versioning, automated evaluation, and continuous improvement Build reusable frameworks, developer tooling, and platform capabilities that accelerate AI innovation across ...

take AI agents from research to production Strong knowledge of cloud platforms (e.g., AWS, GCP, Azure), containerization technologies (e.g., Docker, ECS, Kubernetes), and MLOps practices Proficiency in databases (e.g., PostgreSQL, MongoDB) and caching systems (e.g., Redis, Memcached) for scalable data storage and retrieval Familiarity with Python for collaborating on machine … systems for latency, scalability, and cost, applying caching, load balancing, and other performance techniques to support high-volume inference workloads Advocate for and implement MLOps best practices, including monitoring, logging, tracing, automated retraining, and model/prompt versioning to ensure robust and reliable AI systems Build reusable platforms or frameworks ...