Design, build, and operate a Kubernetes-based MLOps platform using Helm, Terraform, Kubeflow, KServe, vLLM, and JupyterHub. The platform supports the full model lifecycle—experimentation, deployment, and scalable inference—for AI, ML, and LLM workloads in production.

You'll build and operate a Kubernetes-based AI/ML platform using Helm, Terraform, Kubeflow, KServe, vLLM, and JupyterHub. This role focuses on creating infrastructure, tooling, and guardrails so MLOps and data science teams can reliably deploy and serve models at scale.

You will work with Python, LLMs, agentic workflows, retrieval-augmented generation (RAG), and internal AI platforms. The Tactical AI Automations team deploys and supports AI tools across PIMCO's Portfolio Management and Analytics groups, including operational support for Lane Assist and POET (portfolio optimization tools).

Lead 6-7 engineers on core ML infrastructure for a systematic quant fund: distributed model training, LLM hosting/fine-tuning, and scalable deployment. Use Python with working knowledge of C++/Java. Own technical direction for real-world, high-stakes systems.

You'll work with Python, NLP/LLMs, SQL/NoSQL databases, and cloud infrastructure, designing retrieval, knowledge-base, and reasoning models. You own projects end-to-end at Catapult, which builds personalised AI that understands a user's full work context across email, calendar, Slack, and other tools. No team size is given for this founding role.

Waymo★3.0 (61)· London

You will use Python, deep learning frameworks (JAX, TensorFlow/PyTorch), and C++ (preferred) to develop data-selection algorithms and scaling laws for foundation model training. The AI Foundations team builds machine learning solutions for autonomous driving, focusing on reinforcement learning, generative modeling, and robust evaluation to improve the Waymo Driver. You will report to a Senior Staff Software Engineer.

EXL Service★3.7 (2k)· London

You will work with Python, FastAPI, LLM frameworks (prompt engineering, RAG, agentic systems), and cloud AI platforms (Azure OpenAI/AWS Bedrock/Claude) on Kubernetes. As a Sr. AI Architect & Client Partner at EXL, a data and AI services company, you will design and deliver production-grade Generative and Agentic AI systems for enterprise clients, converting ambiguous mandates into secure, scalable solutions while shaping deals and reusable IP.

Work with Python, PyTorch (or JAX/TensorFlow), RL fine-tuning frameworks like TRL or verl, and distributed multi-GPU training. The Reinforcement Learning Team advances RL, Bayesian optimisation, AI agents, LLMs, and vision-language models, applying them to AI for science, chemistry, physics, and robotics while publishing at top venues.

You will work with Python, LLM agents, inference/serving, reinforcement learning, multi-agent orchestration, search/retrieval pipelines, and modern AI coding tools. As a DeepMind Research Engineer, you bridge research and scalable systems, collaborating with scientists on machine forecasting to build 0-to-1 infrastructure, optimize LLM architectures and RL pipelines, and engineer evaluation platforms for autonomous reasoning under uncertainty.

This role develops generative AI applications using Java, Python, React or Angular, LangChain, LlamaIndex, Hugging Face, Docker, Kubernetes, and AWS, with tools like GitHub Copilot. You will build scalable, AI-powered solutions for Citi’s global finance operations.

You will use Python, KQL, SPL to build detection logic and automate response. You will protect Writer’s AI platform, training data, and model deployments, detecting threats like prompt injection and model extraction. You will coordinate incident response across teams and hunt for threats in GPU clusters and distributed training environments.

Design and develop next-generation Vision Transformers and multimodal LLMs using PyTorch. Work on multimodal alignment, representation learning, and large-scale distributed training for image, video, audio, and text datasets. Join a research organization advancing multimodal AI and embodied intelligence.

Lead a hands-on founding engineering role building AI agents for data privacy compliance at Pyxos. Own the control plane, agent architecture, model-abstraction layer, telemetry, evaluation harnesses, and expert knowledge system using LLMs, multi-step agentic workflows, and CI/CD pipelines. You will work with a production system serving enterprise customers, hiring a deliberately small team as the most senior technical leader reporting to the CEO.

You will work with Python and JAX, contributing to the Gemini large-scale pretraining project at DeepMind. Responsibilities include improving training workflows, tooling, and codebase to support research and address issues in large-scale runs.

Work with Python, modern ML tooling, and LLM inference infrastructure to build and deploy specialized AI agents. You will own the end-to-end AI/ML stack for a research-led company that builds simulations and language models for social science and public policy problems.

DeepMind★5.0 (2)· London

Work with Python, LLM agents, LLM inference/serving, reinforcement learning for LLMs, multi-agent orchestration, and modern AI coding tools. The Research Engineer builds and scales infrastructure, models, and tools for the Strategic Bets team, transforming research hypotheses into experiments focused on autonomous systems for machine forecasting.

DeepMind★5.0 (2)· London

You will work with Python and ML libraries such as JAX to solve time-sensitive issues in large-scale training runs. You will improve technical workflows and tooling across Gemini large-scale pretraining, a core DeepMind project advancing AI development.

White & Case★4.0 (120)· London

Works with Python, JavaScript, MCP, agentic AI frameworks, LLMs, and cloud platforms (Azure, AWS) to architect and deploy AI tools. As technical lead of the Applied AI Lab, the role oversees design, development, testing, and deployment of AI solutions to support legal practice and firm-wide innovation. Manages and develops a multidisciplinary team of legal engineers.

You will design and productionise retrieval and knowledge-base systems, reasoning models, and live-screen-context models using Python, cloud infrastructure, and SQL/NoSQL databases. The company builds a proactive AI assistant that maintains a living model of a user’s entire digital workspace—email, messages, meetings, documents—to act on their behalf. This is a founding role in a small team of repeat founders with a product in private alpha.

You'll work with PyTorch, distributed training, Docker/K8s/IaC, and Python backend. The company builds foundation models for extreme physics to accelerate progress in semiconductors, aerospace, defence, and fusion. Joining a small, research-heavy team, you'll be the first dedicated engineering owner for the ML stack.

Work with Python, LLM agents, LLM inference/serving, reinforcement learning, multi-agent orchestration, search/retrieval pipelines, simulation environments, and modern AI coding tools (e.g., AGY, Claude Code, Cursor). Design, build, and scale infrastructure for frontier machine-forecasting systems, optimizing LLM agent architectures and RL pipelines to enable autonomous systems that anticipate future events and reason under uncertainty.

You will work with Python, JavaScript, Model Context Protocol (MCP), agentic AI frameworks, LLMs, and cloud platforms (Azure, AWS). As technical

Work with Python, JAX, and ML/scientific libraries on Gemini large-scale pretraining. The team enables new research, responds to time-sensitive issues in large-scale training runs, and improves tooling and workflows for model architecture and data pipelines.

You’ll work with Python, pandas, scikit-learn, PyTorch, Hugging Face, and LLMs (fine-tuning, prompt engineering). The company builds an AI operating system for industries with fragmented legacy data, producing applied ML models for forecasting, optimisation, and recommendation.

You will work with Python, SQL, OpenAI, Azure OpenAI, LangChain, RAG architectures, vector databases (Pinecone, FAISS, Azure AI Search), Azure, AWS, Databricks, and CI/CD tools. As part of Finalto’s AI Innovation team, you will design and deliver AI-powered workflows—such as document processing, bank statement parsing, and news summarisation—that replace manual processes across the business.