26 to 50 of 197 MLOps Jobs in London

Unix environments, Git, and testing practices Understanding of data governance and security best practices Desirable Skills Experience using GPUs in cloud environments Knowledge of MLOps, CI/CD pipelines, and monitoring ML models Experience building APIs and working with modern DevOps practices Eligibility Requirements Due to the nature ...

engineering initiatives Mentor and support junior engineers within the team Contribute to engineering standards, reusable patterns, and AI best practices Help shape scalable MLOps and AI deployment approaches Drive high-quality engineering practices across testing, deployment, monitoring, and optimisation Tech environment Key technologies include: Python Azure Databricks Generative AI/ ...

domains. Ability to lead architectural discussions and technical trade‐offs without losing sight of the details. Familiarity with cloud infrastructure (AWS preferred) and modern MLOps practices. Commitment to people development: mentoring, performance conversations, and building high‐performing teams. Bonus: experience with NLP on financial contracts, RAG or LLM‐based applications ...

Experience managing AI systems in production environments or high scale data and ML platforms/li , li (Desirable) Experience in working with teams in MLOps, DevOps, or large scale compute environments (e.g., GPU clusters, cloud orchestration, Kubernetes)/li , li (Desirable) Experience with Generative AI evaluation, agent testing ...

analytical and conceptual skills, strong customer focus and mental agility with a results orientation • Sound understanding of GenAI solution constructs e.g., LLMs, RAG, Guardrails, MLOps and multi-modality • Experience managing and executing data science and AI projects, from ideation to deployment, while ensuring alignment with business objectives and delivering impactful ...

Design and implement Onsera's agentic AI platform – architect LLM/agent framework selection, standardized patterns for tools, memory, guardrails, evaluation, and observability Define MLOps protocols for agentic systems – environment separation, versioning of prompts, tools, and policies, cost controls, rate-limiting, and fail-safe mechanisms Bridge data science and platform ...

recommendation systems Familiarity with scientific, biomedical, or scholarly datasets Experience with knowledge graphs, ontologies, or semantic enrichment systems Exposure to production ML systems and MLOps practices Publications or applied research contributions in NLP, IR, search, or generative AI Experience building AI systems in regulated, high-trust, or content-rich domains ...

Scikit-learn, PySpark) Experience with LLM systems (RAG, prompt engineering, orchestration frameworks) Knowledge graphs (Neo4j or similar) End-to-end ML life cycle experience (MLOps, CI/CD, MLFlow, Docker, etc.) Consulting & Commercial Background in consulting or client-facing delivery roles Experience handling stakeholders and translating complex requirements Exposure ...

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 ...

analytics alongside AI solutions). Technical Scope Platform-agnostic engineering mindset; able to work across varied client stacks. Experience with cloud AI services and MLOps/LLMOps (e.g., Azure AI Foundry, AWS SageMaker and/or Bedrock). Strong software engineering practices: Python, APIs, containerisation, CI/CD, IaC, testing ...

GenAI solutions, from data preparation through to production deployment. Designing and building robust ML pipelines, CI/CD processes and MLOps frameworks. Leading GenAI initiatives such as document processing, LLM fine-tuning, prompt engineering and orchestration using modern frameworks. Organising and processing large, complex datasets to enable ...

vector databases AI agents and multi-agent orchestration Python, PyTorch, TensorFlow, Scikit-learn Cloud AI platforms such as AWS, Azure, or GCP Production deployment, MLOps, and scalable AI infrastructure API integration and workflow automation Bonus Points For MCP/Model Context Protocol experience Fine-tuning and evaluation frameworks Recommendation systems ...

hardware or science‐focused teams. Exceptional communication and leadership skills. Nice‐to‐Have Prior experience in startups or multi‐disciplinary product environments. Familiarity with MLOps and embedded systems integration. Background or interest in scientific/biological domains. Our Tech Stack (Current & Planned) Frontend: React, TypeScript Backend: Node.js, Python, REST & GraphQL ...

example the EU AI Act, NIST AI RMF, or ISO/IEC 42001 Hands‐on familiarity with model evaluation, prompt evaluation, RAG architectures, MLOps concepts and observability for AI systems (essential at Senior and Lead levels) Knowledge of partner platforms — agentic workflow tooling, hyperscaler AI platforms (AWS, Azure, Google Cloud ...

personalization Forecasting and optimization Ensure models are scalable, explainable, and production-ready Work closely with engineering teams to operationalize models within production environments (MLOps) Product & Innovation Partner with Product, Engineering, and Architecture teams to embed AI capabilities into SBS solutions Identify opportunities to leverage AI/ML to enhance banking ...

Deep knowledge of LLM APIs, prompt engineering, and conversational AI patterns. Experience in fine-tuning LLMs for domain-specific enterprise applications. Strong expertise in MLOps and LLMOps, ensuring scalable, reliable, and monitorable model deployments. Experience with agentic frameworks and autonomous agent architectures. Proficiency in Python and modern software development practices ...

Trips vertical. Talent Multiplier: Mentor and coach senior and mid‐level ML scientists. Foster a culture of technical excellence, driving best practices for MLOps, rigorous A/B testing, data privacy, and code quality. Skills & Experience Education: Ph.D. or Master’s degree in Computer Science, Machine Learning, Statistics ...

curated data layers. Establish and enforce standards for coding, CI/CD, testing, and deployment across all data delivery teams. Ensure consistent implementation of MLOps & MLflow for model lifecycle management and reproducibility. Establish & Lead Data Solution Architecture practice to ensure design integrity and proper execution of data architecture patterns. Lead ...

into AI-led solutions Advise stakeholders on AI strategy, feasibility, and roadmap Leadership: Guide and mentor AI/engineering teams Set best practices across MLOps/LLMOps/AI governance Contribute to reusable frameworks and accelerators What youll need (must-have): Experience as an AI Architect/ML Architect/ ...

validating continuous monitoring frameworks for AI models. Experience with Azure Databricks, MLflow, Azure ML Services or similar platforms. Experience working within DevOps or MLOps environments. Experience with Docker and Kubernetes. Existing network within the UK AI, analytics or technology ecosystem. ...

Competencies Exposure to AI governance, security, or compliance considerations. Experience deploying ML models in cloud environments (Azure preferred, AWS acceptable). Familiarity with MLOps practices (model versioning, evaluation, monitoring, iteration). Experience working with non‐ideal, real‐world data rather than curated research datasets. Experience working in both enterprise ...

assessment flows, or dashboard‐based visibility.* Exposure to AI FinOps, such as cost‐aware model selection, unit economics, or prompt‐efficiency practices.* Experience with MLOps or AI delivery tooling, or with AI‐specific observability systems.* Participation in industry communities or standards bodies, with the ability to translate external practice into ...

track record of developing and deploying models at scale Expertise in Python, ML frameworks (TensorFlow, PyTorch), and experience with cloud platforms Strong 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 ...

LLMs and RAG architectures. Python: Strong proficiency alongside PySpark for data engineering. Azure: Deep familiarity with native tools (AI Search, Document Intelligence). MLOps: Understanding of LLMOps frameworks for production deployment. 6 Months Remote Contract | £600 a day inside IR35 | Active Security Clearance Are you a seasoned technical leader with ...

LLMs and RAG architectures. Python: Strong proficiency alongside PySpark for data engineering. Azure: Deep familiarity with native tools (AI Search, Document Intelligence). MLOps: Understanding of LLMOps frameworks for production deployment. 6 Months Remote Contract | £600 a day inside IR35 | Active Security Clearance Are you a seasoned technical leader with ...