21 of 21 Part-Time MLOps Jobs in London

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

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

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

including LangChain and LangGraph, agent frameworks, vector databases, FastAPI, and cloud platforms such as AWS, Azure, GCP or Databricks. Familiarity with CI/CD, MLOps and LLMOps practices is also expected. Strong communication skills and the ability to engage effectively with stakeholders across technical and business functions are important ...

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

maintain ML solutions on modern frameworks to meet functional business requirements, adhering to software engineering best practices and with exposure to version control, testing, MLOps, CI/CD and API design \n Hands on experience in using one of 3 major cloud technologies (AWS, Azure or GCP) in a production ...

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

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

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

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

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

nResearching, advising, building, deploying and testing models from day one\n \n \nWork as a bridge between the data science and MLOps departments to continue bring understanding and best practice deployment techniques into the Data Science team\n \n \nHands on big project ownership\n \n \nEveryday modelling and Data ...

implementation and monitoring of solutions\n \n \nfacilitate adoption of ML models through exceptional communication with stakeholders\n \n \nwork in an established MLOps team at SB there is significant opportunity to deploy end to end and will be actively encouraged\n \n \ncontinually improve through active learning and development ...

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

Strong understanding of various architectures including data warehouses, data lakes, data lake houses and data mesh \n Strong understanding of best practice DataOps and MLOps \n Up-to-date understanding of various data engineering technologies including Apache Spark, Databricks and Hadoop \n Strong understanding of agile ways of working ...

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

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

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

happy to discuss flexible working! \n \n Top 3 skills needed for this role: \n \n Proven machine learning expertise \n Strong MLOps and deployment capability \n Demonstrated stakeholder and influence skills \n\n What this role is all about: \n At Vitality, data scientists and AI Engineers develop … Proven track record of deploying AI applications into production environments \n Strong understanding of API development, microservices, and containerization (Docker, Kubernetes) \n Knowledge of MLOps best practices \n Excellent problem-solving skills and ability to work in a fast-paced environment \n\n So, what ...

into the engineering lead and collaborating to set strategy and direction. AIE comprises two sub-teams: a platform team that builds the AI and MLOps capabilities developers across ICB use to take models and agents from experiment to production, and a team that builds internal AI agents and assistants that … Bias to action; executes with urgency, discipline, and quality. \n\n Preferred qualifications, capabilities & skills \n \n Product management experience for AI/ML, MLOps, or data platforms. \n Familiarity with MLOps concepts and frameworks, such as MLflow, Amazon SageMaker, and feature stores. \n Familiarity with AI concepts, such ...

solutions \n Leverage foundational libraries and services for re-use across teams \n Apply best practices in software engineering and computer science \n Utilize MLOps tools for versioning, reproducibility, and observability \n Align ML problem definitions with business objectives \n Mentor and support team members, with optional management responsibilities … with higher-level interfaces like Pydantic AI and Langraph \n Strong understanding of computer science fundamentals and development best practices \n Broad knowledge of MLOps tooling for versioning, reproducibility, and observability \n Ability to understand business objectives and align ML problem definitions \n\n Preferred Qualifications, Capabilities, and Skills ...