You will work with Python, RDKit, Biopython, OpenMM, MDAnalysis, docking, virtual screening, SAR, pharmacophore, QSAR, ADMET, and protein-ligand modeling tools. You will guide, evaluate, and improve AI-driven drug discovery workflows—reviewing agent-generated optimization plans, compound prioritizations, and modeling workflows—and lead computational chemistry strategy for live drug programs.

This role involves working with Python, PyTorch, JAX, CUDA, Linux, Docker, Kubernetes, Slurm, Ray, Flyte, and Argo. The team builds ML and physics-based infrastructure for drug discovery, including simulation workflows and agent-usable scientific pipelines. Engineers improve code quality, GPU efficiency, and distributed execution while making research tools scalable and maintainable.

Work with Python, Snowflake, Power BI, Git, CI/CD, and cloud deployment to productionize data science and AI prototypes. You will bridge raw data ingestion to analytics-ready assets, building and maintaining analytics workflows and dashboards for a global investment firm.

Work with TensorFlow or PyTorch, Python, and related computer vision/graphics libraries on neural network architectures (convolutional, recurrent, transformer, diffusion-based). The Visual Computing Group (VCG) researches and develops generative models for game rendering and live video streaming to advance state-of-the-art runtime efficiency, visual quality, and latency.

Use Python, SQL, graph databases, Docker, Kubernetes, and LLMs to own Kharon’s risk-propagation engine that evaluates entities in a knowledge graph and surfaces risk signals. The role spans research, data engineering, and product to evolve a production system for global security analytics.

Use Python, SQL, BM25, vector search, reranking, and RAG pipelines to evolve Kharon's Corestream risk intelligence feed—a platform surfacing entity-level insights and topic-based risk narratives, integrating graph-based outputs into semantic search and narrative generation.

Lead a team of Data Scientists and ML Engineers building AI solutions for financial markets using LLMs, Generative AI, Deep Learning, Transformers, Agentic AI, Python, PyTorch, TensorFlow, Hugging Face, LangChain, Semantic Kernel, Azure AI Foundry, AWS AI Services, and MLOps/LLMOps practices.

You will own Relay’s Demand Forecasting models, working hands-on with Python, ML frameworks, and forecasting tools to predict parcel volume, weight, and size at outcode granularity out to D30. You set the technical direction for the Network squad’s forecasting engine, which drives sortation, middle-mile, last-mile, and expansion decisions. The centralised data team has ~30 people, with you acting as the senior IC anchor, mentoring a Senior Data Scientist and an Analyst.

You will build AI agents using Python, SQL, Docker, Kubernetes, AWS (RDS, EKS, S3), GitHub Actions, RAG pipelines, BM25, vector search, hybrid retrieval, and knowledge graphs. These agents interpret geopolitical and financial signals for Kharon, a company providing global security risk intelligence.

Work with Python, SQL, BM25, vector search, reranking, RAG pipelines, agentic systems, and graph-aware search. Own and evolve Corestream, Kharon’s risk intelligence feed that surfaces entity-level insights and topic-based risk narratives for global security clients.

Infosys★3.8 (17k)· London

You will architect CI/CD pipelines using the Harness platform (CI, CD, STO, Chaos Engineering, CCM, AI Agents), Kubernetes, Terraform, Helm, OPA, and cloud services. As a Harness SME, you drive DevOps transformations, migrating enterprise teams from legacy tool chains and implementing DevSecOps governance.

You'll work with Python, APIs, MCP servers, AI harnesses, agentic operating systems, and enterprise SEO/visibility toolsets. This role extends Jellyfish's live agentic delivery stack internally and helps enterprise clients build technical foundations for agent-driven search and GEO.

Develop novel quantum algorithms for computing topological and geometric invariants of data, implement them on Quantinuum’s quantum hardware using pytket, Guppy, or Qiskit, and optimize performance under realistic hardware constraints with error mitigation and quantum error correction. The role contributes to advancing quantum computer capabilities for data analysis in the near-fault tolerant era.

Work with Python, LLMs, RAG pipelines, vector databases, Hugging Face Transformers, LangChain, PyTorch, and Docker to build production AI systems. Design, develop, and deploy AI-powered solutions such as conversational AI, document intelligence, and backend services for the Global Analytics team.

This training program teaches Python, AI model development, and AI application building, leading to Azure AI Fundamentals and CompTIA Data+ certifications. It prepares entry-level learners for a role as an AI or data professional through coursework and a job guarantee.

You will study Python, AI models, developing AI applications, Azure AI Fundamentals, Azure Data Fundamentals, and CompTIA Data+. Newto Training offers a 130-hour course with real-world projects to prepare you for an entry-level AI or data role, and guarantees job placement upon completion.

You will work with AWS, Python, SQL, ETL/ELT pipelines, data warehousing, and APIs. You will own the data platform for a company developing AI and machine learning products that help enterprise clients make decisions. You will start as a senior individual contributor with the opportunity to build and lead a future data team.

This is a paid training course, not a job. You will learn Python, Azure AI Fundamentals, Azure Data Fundamentals, CompTIA Data+, and CompTIA AI Prompting Essentials through project work to prepare for entry-level AI roles. Newto Training provides the coursework and recruitment support.

You will study AI Fundamentals, Python, AI models, and AI application development, earning certifications including Azure AI Fundamentals, Azure Data Fundamentals, and CompTIA Data+. This training program prepares you for entry-level AI or data roles through project work and recruitment support.

This listing is for a paid AI training course, not a job, covering Python, AI models, AI application development, and data methodologies, leading to certifications like Azure AI Fundamentals and CompTIA Data+. Newto Training provides the course and recruitment support to help graduates secure entry-level AI or data positions.

Lead TypeScript and Python development, CI/CD, and API-first design to embed AI into production software platforms. You will modernize legacy systems toward service-based architectures and guide integration with AI services like MCP, working across DevOps, platform engineering, and data science teams.

Monzo★3.5 (108)· London

You will work with LLMs, autonomous agents, RAG, sequence-based architectures, Python, SQL, and Go, developing and deploying machine learning models in production. You’ll lead the design of a human-in-the-loop system that predicts, identifies, and routes customer problems to scale support for Monzo’s banking products across multiple geographies.

Monzo★3.5 (108)· London

You will build and deploy deep learning, graph-based, and sequence-based ML models using Python and SQL, with exposure to Go. You will design human-in-the-loop systems and autonomous agents to route, prioritise, and automate customer-support issues for millions of customers across thousands of support staff at Monzo. You will be one of four ML engineers embedded in product squads.

You will work with Python, AI coding agents (e.g., Claude Code, Codex), and potentially AWS, Kubernetes, and Temporal. Unitary builds Virtual Agents — AI-powered software workers that automate complex workflows end-to-end in customer tools, focusing on commercial insurance, marketplaces, and primary healthcare.

Cint★3.5 (13)· London

Use Python and SQL for statistical modeling and machine learning to optimize Cint's programmatic research marketplace (Cint Exchange). You will analyze marketplace dynamics, conduct A/B tests, and build predictive models to guide product and pricing strategy.