201 to 225 of 807 MLOps Jobs in the UK

applied AI Desirable Experience Hands-on experience with AWS services such as Lambda, ECS, S3, SageMaker or Bedrock Knowledge of ML engineering and MLOps practices (e.g. Docker, MLflow) Experience working in government, defence or other highly regulated environments Proven application of large language models (LLMs) to real-world problems Security ...

databases including Pinecone, Weaviate, Chroma, FAISS or Milvus.Experience building Retrieval Augmented Generation (RAG) solutions.Understanding of AI governance, Responsible AI and AI security principles.Exposure to MLOps, model deployment and monitoring frameworks.Experience working within a regulated industry such as Financial Services, Professional Services, Insurance or Consulting.What We're Looking ForA naturally curious ...

databases including Pinecone, Weaviate, Chroma, FAISS or Milvus.Experience building Retrieval Augmented Generation (RAG) solutions.Understanding of AI governance, Responsible AI and AI security principles.Exposure to MLOps, model deployment and monitoring frameworks.Experience working within a regulated industry such as Financial Services, Professional Services, Insurance or Consulting.What We're Looking ForA naturally curious ...

customer conversations — discovery, whiteboarding, architecture reviewsFamiliarity with one or more: data engineering (ETL/ELT, medallion architecture, streaming), data science/ML (model training, MLOps), or SQL analyticsStrong presentation and demo skills — you will build and present a live solution during the interviewBachelor's or Master's degree in Computer ...

Bedrock, SageMaker, OpenAI, Cognitive Services). Strong Python programming skills with hands‐on experience using frameworks like Autogen and LangGraph . Solid grounding in MLOps , containerisation (Docker, Kubernetes), and vector databases. Understanding of agent monitoring tools (Langfuse, OpenTelemetry). Strong software engineering best practices (testing, CI/CD, code reviews ...

machine learning principles, deep learning architectures, and natural language processing, particularly with large language models. Experience with cloud platforms (e.g., AWS, GCP, Azure) and MLOps tools for managing the AI lifecycle. Demonstrated ability to write clean, efficient, and well-tested code for scalable software systems. Exceptional problem-solving skills ...

embed these controls into enterprise architectures. Expertise in at least one major cloud platform (AWS or Azure), together with strong software engineering, security architecture, MLOps, and LLMOps knowledge. Next steps Computershare are dedicated to providing you with the opportunity to succeed on your own merits, starting from the application process. ...

policy considerations in applied AIDesirable ExperienceHands-on experience with AWS services such as Lambda, ECS, S3, SageMaker or BedrockKnowledge of ML engineering and MLOps practices (e.g. Docker, MLflow)Experience working in government, defence or other highly regulated environmentsProven application of large language models (LLMs) to real-world problemsSecurity ClearanceThis role ...

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 ...

frameworks, and scalable AI/ML platforms.Mastery of enterprise cloud stacks (AWS/GCP/Azure), vector DBs, Knowledge Graphs, contextual memory, distributed systems, MLOps/LLMOps, and infrastructure automation.Additional infoYOUR TRAVELMostly from the BCG office, occasionally work at other BCG offices.Boston Consulting Group is an Equal Opportunity Employer. ...

least oneDeep experience with distributed computing with Apache Spark and knowledge of Spark runtime internalsFamiliarity with CI/CD for production deploymentsWorking knowledge of MLOps, ML/AI models and AI APIsDesign and deployment of performant production end-to-end data architectures and applications that combine data pipelines, ML/ ...

statistical methodologies, along with data extraction, manipulation and feature engineering techniquesExperience with Spark, agile delivery methods and CI/CD practicesFamiliarity with DevOps and MLOps tools and practices, such as Docker, Terraform and MLFlowConfidence influencing and communicating with stakeholders across technical and non-technical teamsIdeally, exposure to cloud infrastructure ...

along with data extraction, manipulation and feature engineering techniques Experience with Spark, agile delivery methods and CI/CD practices Familiarity with DevOps and MLOps tools and practices, such as Docker, Terraform and MLFlow Confidence influencing and communicating with stakeholders across technical and non-technical teams Ideally, exposure to cloud ...

exposure in media, or another enterprise vertical. Familiarity with AI safety, responsible AI frameworks, or enterprise AI governance. Familiarity with AI deployment infrastructure and MLOps tooling. #LI-EU Locations London Additional Information Equal Employment Opportunity Statement All employment decisions shall be made without regard to age, race, creed, color, religion ...

problem‐solver at heart. Nice to Haves (but we can teach you): You've already worked with Rust in a production environment. Experience with MLOps, particularly the challenges of managing the lifecycle of models in a multi‐tenant, high‐availability system. Familiarity with building ML inference services, model serialization (e.g. ...

datasets (scientific, biomedical, or enterprise data) Familiarity with scientific ontologies and metadata standards (e.g., MeSH, UMLS, ORCID, CrossRef) Exposure to production ML systems and MLOps practices Familiarity with data visualization and analytical tooling (e.g., Tableau, Power BI, matplotlib, seaborn, or similar) to communicate insights effectively Experience with human ...

data‐driven systems in production or near‐production settings. Experience with generative AI systems, LLM‐enabled applications or evaluation pipelines. Experience with cloud platforms, MLOps tooling, model serving or platform engineering. Advanced use of Linux, Docker, version control, CI/CD and service‐oriented deployment practices. Experience working across ...

working with AWS-native data services and event-driven architecturesExperience building data products that support analytics, reporting, machine learning, or AI use casesKnowledge of MLOps, AI Engineering, Agentic AI, or Generative AI implementation patternsExperience leading distributed engineering teamsExperience establishing engineering standards, reusable frameworks, and community-of-practice initiativesKnowledge of data ...

working with AWS-native data services and event-driven architecturesExperience building data products that support analytics, reporting, machine learning, or AI use casesKnowledge of MLOps, AI Engineering, Agentic AI, or Generative AI implementation patternsExperience leading distributed engineering teamsExperience establishing engineering standards, reusable frameworks, and community-of-practice initiativesKnowledge of data ...

ability to apply automation responsibly to real delivery and operational problems. You might come from data engineering, platform engineering, software engineering, SRE, analytics engineering, MLOps, or cloud infrastructure. What matters most is that you enjoy reducing toil, improving developer experience, and building secure, observable systems that other teams can depend ...

decoding)Proven track record of developing and deploying models at scaleExpertise in Python, ML frameworks (TensorFlow, PyTorch), and experience with cloud platformsStrong familiarity with MLOps principles, CI/CD pipelines, and containerization (Docker, Kubernetes) With preferred backgrounds covering some of the following:Experience with speech-to-text or text ...

such as PostGres, Oracle DB or MySQLExperience working within a team, in an agile, iterative engineering environment.Experience deploying and managing machine learning model deployments (MLOps) on appropriate services. Examples include (but not limited to) Azure Machine Learning, AWS Sagemaker, Databricks or Snowflake.Experience using AI or Agentic techniques in application ...

Jira for managing tasks and sprint planning. Certifications in cloud technologies, such as AWS Certified Developer. Manage, host, and serve AI/ML models (MLOps), by ensuring their scalability, reliability, and performance in production environments. Location: Candidates based in Spain: flexible remote work, with availability to travel ...

controlled vocabularies, and metadata standards.Experience with NLP, entity extraction, entity linking, semantic enrichment, search, ranking, recommendation, or knowledge graph methods.Exposure to production ML systems, MLOps, data pipelines, and model monitoring.Work in a way that works for youWe promote a healthy work/life balance across the organization. We offer ...

experience.If you don't have demonstrable DDD, spec driven experience on your CV, you will be rejected. This is not an AI research or MLOps role. We're looking for software engineers who have demonstrable experience shipping production-grade AI-native applications and who use AI as part of both ...