1 to 25 of 34 MLOps Jobs in Central London

experimentation, pragmatic engineering, and rapid iteration are key to creating business value. Leverage modern tools and methods: Use contemporary ML frameworks, cloud platforms, and MLOps best practices to build scalable, reusable solutions. Communicate insights clearly: Distill complex technical findings into concise, actionable narratives for technical and business audiences alike. Keep ...

/DL libraries, and deliver actionable insights. Visualise results and tell stories through dashboards (Tableau/Power BI). Operationalise models using MLOps, version control, containerisation, and cloud platforms. Support proposals, roadmaps, partner evaluations, and proof‐of‐concepts. Advocate for responsible AI practices and compliance. Who You Are Required ...

testing, and statistical evaluation. Proven track record transitioning models from experimentation to production with governance and quality controls. Experience with experiment tracking and MLOps tooling (MLflow, Weights & Biases, DVC). Experience developing LLM/GenAI applications, including prompt engineering and RAG architectures. Familiarity with LLMOps tooling (LangSmith, LangChain, LangGraph). ...

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 ...

security clearance; sponsorship cannot be provided. Desirable: Knowledge of AWS services (e.g., Lambda, ECS, Bedrock, S3, SageMaker). Experience with ML engineering and MLOps (e.g., Docker, MLflow). Experience in regulated industries or government-related projects. Applying LLMs or advanced AI methods to real-world problems. Benefits: Competitive salary ...

particularly Vertex AI, Google Kubernetes Engine (GKE), and Cloud Functions. Databases: Strong command of relational databases like PostgreSQL and familiarity with NoSQL solutions. MLOps & DevOps: Production experience with Docker, Kubernetes, CI/CD pipelines (e.g., Jenkins, GitHub Actions), and Infrastructure as Code (Terraform). Qualifications Bachelor's or Master ...

permitted; govern enterprise knowledge ingestion. Architecture & Integration: Distributed systems, microservices, cloud‐native (Azure/AWS/GCP); secure API design (REST/GraphQL). MLOps & AIOps: CI/CD for AI, model lifecycle (eval, versioning, rollout/rollback), guardrails, telemetry, drift monitoring, prompt safety. DevSec & Responsible AI: Privacy‐by‐design ...

Data Pipeline Engineering: Build and maintain robust data pipelines for feature extraction, transformation, and model training. Deployment & Monitoring: Implement models into production environments using MLOps best practices (CI/CD, containerization, monitoring). Performance Optimization: Continuously improve model accuracy, latency, and scalability. Collaboration: Work cross-functionally with product managers … TensorFlow, PyTorch, Scikit-learn), and experience with SQL. ML Expertise: Solid understanding of supervised/unsupervised learning, deep learning, and model evaluation techniques. Cloud & MLOps: Experience with AWS, GCP, or Azure; Docker/Kubernetes; MLflow or similar tools. Data Handling: Familiarity with big data frameworks (Spark, Hadoop) and data versioning ...

Strong hands-on expertise with LangGraph Experience with knowledge graphs, RAG and LLM tool orchestration Proven software engineering fundamentals (Python preferred) Solid MLOps grounding: Kubernetes, CI/CD, Docker, cloud platforms (GCP/AWS/Azure) Comfortable presenting, teaching, running workshops and mentoring engineers Strong communicator able to simplify complex ...

Services, and Azure Machine Learning. Familiarity with Azure Databricks is a plus. Understanding of CI/CD pipelines, containerization (Docker, Kubernetes), and experience implementing MLOps practices using Azure DevOps. Qualifications such as the following would be advantageous, however not necessary: Microsoft Azure AI Engineer Fundamentals/Associate Microsoft Azure Data ...

Databricks, LangChain , vector databases , or LLM orchestration tools . Knowledge of RAG pipelines , transformers , and modern AI architectures . Understanding of deployment or MLOps best practices (Azure experience a plus). Recent, hands-on experience with applied LLM projects . Strong communication and stakeholder engagement skills. A Master’s degree ...

deploying models and working with cloud platforms (AWS preferred) Ability to communicate complex technical concepts clearly Desirable AWS services (SageMaker, ECS, Lambda, S3, Bedrock) MLOps/ML engineering experience (Docker, MLflow) Experience in government or regulated environments Practical experience applying LLMs Security Clearance UKIC DV eligible required ...

junior team members Skillset: Strong Python and ML libraries (scikit-learn, PyTorch, pandas, NumPy) Experience with AWS cloud services (SageMaker, Lambda, S3) Familiarity with MLOps tools (MLflow, Weights & Biases) Knowledge of NLP, computer vision, or time-series analysis Ability to design experiments and apply statistical validation Understanding of Responsible ...

orchestration, and agentic architectures Proven experience delivering ML/AI systems into production environments Excellent Python engineering and system design skills Familiarity with APIs, MLOps, and cloud deployment (AWS, GCP, or Azure) Clear communicator, comfortable in fast-paced, early-stage environments STEM degree required; MSc in AI, Computer Science ...

Large Language Models (LLMs) for text analysis, sentiment analysis, and generative AI applications. Build and maintain production-grade ML pipelines using CI/CD, MLOps, and cloud platforms (Azure, AWS, GCP). Conduct A/B testing and randomized control trials to validate model performance and business impact. Perform data … Expertise in NLP techniques and frameworks, familiarity with LLM fine-tuning and deployment. Knowledge of big data technologies and cloud platforms. Strong understanding of MLOps practices, CI/CD pipelines, and model monitoring tools . Proficiency in data visualization tools. Excellent problem-solving skills, attention to detail, and ability ...

coaches, analysts, and execs Highly Desirable Skills Football Analytics Experience : Familiarity with football-specific datasets (event, tracking, positiona l), and libraries like mplsoccer Advanced MLOps & Modelling : Experience with the Vertex AI ecosystem, especially pipelines, and advanced techniques such as player valuation , tactical modelling , etc. Bayesian Modelling : Knowledge of probabilistic programming ...

work from home. The AI Engineer will support delivery of AI use cases, workflows, analytics and solutions - responsible for tasks such as pattern recognition, MLOps, software development, neural networks, and natural language processing on a day-to-day basis. Qualifications Pattern Recognition and Neural Networks skills Computer Science and Software ...

building, training, and deploying machine learning models for classification, regression, clustering, and NLP tasks. Understanding of model evaluation metrics, hyperparameter tuning, feature engineering, and MLOps practices for production deployment. Generative AI & LLM Integration - Proficient in working with Large Language Models including OpenAI GPT models, Anthropic Claude, Azure OpenAI, and open … databases (SQL Server), data validation, cleansing workflows, scheduling tools (Azure Data Factory), and ensuring data quality for machine learning applications. Machine Learning Operations (MLOps) - Experience deploying ML models to production environments using containerisation (Docker), orchestration (Kubernetes), model versioning (MLflow, DVC), monitoring model performance and drift, A/B testing frameworks ...

building, training, and deploying machine learning models for classification, regression, clustering, and NLP tasks. Understanding of model evaluation metrics, hyperparameter tuning, feature engineering, and MLOps practices for production deployment. Generative AI & LLM Integration - Proficient in working with Large Language Models including OpenAI GPT models, Anthropic Claude, Azure OpenAI, and open … databases (SQL Server), data validation, cleansing workflows, scheduling tools (Azure Data Factory), and ensuring data quality for machine learning applications. Machine Learning Operations (MLOps) - Experience deploying ML models to production environments using containerisation (Docker), orchestration (Kubernetes), model versioning (MLflow, DVC), monitoring model performance and drift, A/B testing frameworks ...

deliver AI and analytics solutions aligned with the organisations Data & AI strategy. Key Responsibilities End-to-end development of AI/ML solutions. MLOps practices: CI/CD, model monitoring, retraining. Use of open-source and enterprise tools (LangChain, Azure OpenAI, Databricks) click apply for full job details ...

Azure OpenAI, Prompt Engineering, Cognitive Search, Form Recognizer. Data/Coding: Expert-level Python, PySpark, and Vector DB integration. Ops: Practical knowledge of MLOps and LLMOps lifecycles. Security: Must hold active UK Government SC Clearance. Certifications Mandatory: AZ-204 (Azure Developer Associate). Preferred: AZ-305 (Solutions Architect ...

. Practical experience with LLMs, RAG architectures, embeddings and transformers. Experience deploying models using cloud infrastructure and working with microservices or APIs. Familiarity with MLOps concepts such as monitoring, evaluation and continuous improvement. Strong communication skills and the ability to translate business problems into AI-driven solutions. A collaborative mindset ...

deliver AI and analytics solutions aligned with the organisations Data & AI strategy. Key Responsibilities * End-to-end development of AI/ML solutions. * MLOps practices: CI/CD, model monitoring, retraining. * Use of open-source and enterprise tools (LangChain, Azure OpenAI, Databricks). * Generative AI features: embeddings, RAG, AI agents. …/ML delivery. * Legal/professional services experience is a plus. * AI/ML frameworks: PyTorch, TensorFlow, LangChain. * Cloud: Azure (preferred), AWS, GCP. * MLOps: CI/CD, model lifecycle, monitoring. * Generative AI: LLMs, RAG, chat agents. * Data engineering alignment: ETL, governance. * Strong coding, communication, and collaboration skills. * Strategic thinking, problem ...

ALTEN , an engineering and technology consultancy, We are a leading Engineering and IT consultancy operating across 30 countries, making waves in all sectors: Aeronautics, Space, Defence, Security and Naval, Automotive, Rail and mobility, Energy and ...

Senior ML Engineer will own the journey from experimentation and hypothesis testing through to secure, production-grade deployment, using a modern AWS-based MLOps and LLMOps platform. The Role The successful candidate will balance rapid experimentation with production readiness, prototyping and validating machine learning and generative AI approaches while ensuring …/B testing and statistical evaluation Proven experience moving models from experimentation into production with appropriate governance and quality controls Hands-on experience with MLOps tooling such as MLflow, Weights & Biases or Data Version Control Practical experience building LLM/GenAI applications, including prompt engineering and retrieval-augmented generation ...