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.

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.

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.

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.

Google★4.3 (6.2k)· London

Manage engineers building Google’s Learning Platforms within the Global Business Organization, which powers Google’s ecosystem of products and services for customers and partners. Work with back-end languages (Java, Python, Golang, C++) and front-end (JavaScript, TypeScript, HTML, CSS), plus ML model fine-tuning and evaluation.

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.

Work with Nvidia GB200 72NVL CX8 and AMD MIxxx architectures, coding in C, C++, C#, Java, JavaScript, or Python to benchmark, profile, and debug GPU clusters. You will help build foundational models powering Microsoft’s personalized AI assistant, Copilot, spanning training and inference infrastructure, model architecture, and data curation.

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'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.

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

Work with Python, PyTorch, scipy, scikit-learn, numpy, pandas, MLflow, Docker, Kubernetes, Ray, and GCP to build and scale distributed compute for research workloads. Phaidra creates AI-powered control systems for the industrial sector using reinforcement learning to enable facilities to learn and improve automatically.

You'll lead a 4–5 person team building machine learning systems using Python, TensorFlow, PyTorch, JAX, AWS, Docker, and Kubernetes for Super's Playstack platform, which powers sports betting and gaming experiences across Europe and Latin America. The role combines technical contribution, project management, and people leadership, focusing on predictive analytics, personalization, fraud detection, and real-time recommendations that enhance customer engagement in online gambling and sports betting.

U.S. Bank★3.5 (12k)· London

Work with CI/CD (GitLab, Jenkins, Harness), Terraform, AWS (ECS/EKS, Lambda, S3, IAM), Docker, Kubernetes, OpenTelemetry, CloudWatch, and Python/Bash. You own the deployment, operations, and reliability of the AI‑SDLC platform as it transitions from local execution to a centralized, hosted enterprise service.

Build and scale data pipelines for large video generation models using AWS S3, DynamoDB, Kubernetes, Python, and PyTorch. You will design annotation workflows across MTurk and Prolific, and train smaller supporting models for data filtering and quality assessment. Cantina Labs is a social AI company developing real-time character models.

At Axon Labs you will own production infrastructure across multi-region cloud and latency-sensitive environments using AWS, Python, C++, Kubernetes, Terraform, CI/CD, PostgreSQL, TimescaleDB, Kafka, Redis, low-latency messaging, Grafana, Prometheus, and Loki. The company is a systematic trading and research lab that builds decision engines running in live markets, with multiple strategies already in production across venues.

The role involves Python, FastAPI, Docker, Kubernetes, CI/CD, MLOps tools (MLflow, Weights & Biases), and workflow orchestration (Airflow, Prefect, Kubeflow) for deploying and monitoring ML models in production. You'll build and maintain ML infrastructure, pipelines, and observability for an edge computing environment at a company scaling its ML platform.

Build and manage CI/CD pipelines (GitLab/Jenkins/Harness), infrastructure as code (Terraform), and AWS cloud services (ECS/EKS, Lambda, S3) with Docker and Kubernetes for the AI‑SDLC platform. Ensure platform reliability, monitoring (OpenTelemetry, CloudWatch), and operational support, while driving the centralized, hosted enterprise service through automation and security compliance. This role is within the team at Elavon, a U.S. Bank subsidiary building tailored payment solutions.

Citi★3.9 (21k)· London

Build and scale AI-powered financial systems using Java, Spring, React/Angular, Python, FastAPI, Docker, and GitHub Copilot within Citi’s global tech environment. You will design generative AI agents, LLM workflows, and RAG pipelines while working on enterprise microservices, mobile, or front-end development. This role is for an individual contributor on an agile team.

Work with AWS, Kubernetes, Slurm, PyTorch, and distributed training frameworks to build foundation model AI for physical intelligence, enabling robots to learn tasks from a single human demonstration. This hands-on role focuses on GPU compute optimisation, cluster scheduling, and high-performance networking within a small, elite team in London.

Build and operate a Kubernetes-native cloud platform for GPU inference workloads, using Python or Go. You'll lead technical designs, own areas like request routing and adaptive scheduling, and implement optimizations such as speculative decoding and KV-cache reuse, working with Prometheus, Grafana, and OpenTelemetry.

Build AI-powered features using Python, SQL, LLM frameworks like LangChain, cloud platforms (AWS, GCP), Docker, Kubernetes, APIs, and real-time systems. You'll work on conversational AI, agentic commerce, and personalisation for Hyperlayer's intelligent banking platform, assisting the Head of AI and mentoring junior engineers.

Build high-throughput data pipelines, deploy LLMs and ML models, and develop AI tooling working with Python, Spark, Databricks, Airflow, Kafka, KDB, AWS/GCP/Azure, and Kubernetes. The role supports a global investment firm where technology drives performance, building data platforms and AI systems directly used by Portfolio Managers to uncover alpha.

Deloitte★3.9 (14k)· London

You will work with Python, JavaScript, Kafka, Azure, Kubernetes, Terraform, Helm, LangChain, PySpark, Databricks, and ADLS. You will design, develop, and deploy production-grade GenAI and machine learning systems for the GenAI team within Deloitte’s Data & AI organisation.

Work with Python, Kubernetes, vLLM, SGLang, and cloud infrastructure (AWS, Azure, GCP) to build POCs, MVPs, and production integrations. As an AI Field Engineer, you embed with customers to deploy and fine-tune LLMs (SFT, DPO, RFT) and architect production inference systems.