Top Tech Jobs & Startup Jobs in London

Leads development of an internal Azure platform for AI workloads, creating golden paths, developer tooling, self-service environments, CI/CD, observability, security guardrails, model access, and evaluation workflows. The role operates between platform engineering and product teams, supporting Kubernetes, infrastructure-as-code, GitOps, LLM integrations, and developer enablement. Responsibilities include maintaining a developer portal, coaching teams, documenting platform capabilities, measuring adoption, and improving delivery speed and reliability.

Own the architecture and roadmap for a production Azure enterprise AI platform supporting model access, agent orchestration, integrations, governance, security, and observability. Define production standards for agents, including evaluation, monitoring, cost controls, identity, data residency, and release processes. Advise senior stakeholders, review solution designs, establish reusable patterns for the AI Centre of Excellence, and guide integration with HR, service management, and enterprise data systems.

Lead a small engineering pod building an internal Skills Marketplace MVP. Design and develop a full-stack application using Kotlin and Spring on the backend and React on the frontend. Collaborate directly with clients and stakeholders, lead day-to-day technical delivery, support adoption, and apply AI tools such as Claude to accelerate solution design. The role requires technical team leadership, full-stack development, user engagement, multidisciplinary collaboration, and a startup-oriented mindset.

Build and operate secure developer-platform products, CI/CD pipelines, infrastructure, observability, and AI-agent tooling in a regulated environment. Responsibilities include prototyping and hardening solutions, designing agent controls, developing dashboards and developer tools, embedding secure defaults, running measurable experiments, improving DORA performance, and maintaining zero-downtime platform operations. The role also involves direct collaboration with delivery teams and influencing the platform roadmap.

Design and execute adversarial tests for AI agents and LLM workflows, covering prompt injection, tool misuse, hijacking, poisoning, privilege escalation, and hallucinations. Develop repeatable evaluation methods for nondeterministic systems, establish AI risk governance, map controls to OWASP, NIST, and MITRE frameworks, and define incident-response procedures. Recommend testing tools, advise teams and clients, and progressively train security engineers to operate the practice.

Lead hands-on development of production Python and FastAPI services for an agentic development platform. Establish automated acceptance and architecture testing, enforce engineering standards, identify AI-agent failure modes, and promote safe use of agentic editors across more than 120 engineers. Collaborate with architecture, cloud, AI, Snowflake, and application teams while coaching engineers and delivering software in ambiguous, fast-moving environments.

Build and optimize scalable full-stack web applications using React, Python, JavaScript, and TypeScript. Deploy cloud-native solutions, preferably on Azure, integrate APIs, microservices, LLM-based services, and agentic AI systems, and support containerized environments and CI/CD pipelines. The role also emphasizes high-throughput applications, front-end performance, observability, and reliable deployments while collaborating with backend, cloud, and AI engineering teams.

Design and implement an AI-enabled AWS delivery platform that safely supports agentic development. Build guardrails, controls, observability, telemetry, and policy enforcement across local development environments and CI/CD pipelines. Research and prototype secure developer workflows, containerized or remotely offloaded environments, and scalable governance practices. Collaborate across engineering, platform, security, and enterprise teams to turn emerging concepts into practical solutions and support developer adoption.

Build and operate a highly available Coder platform on Kubernetes, creating Terraform-defined workspace templates and hardened container images. Implement identity, networking, egress, secrets, and audit controls; troubleshoot Linux and connectivity issues; load test and optimize cost, scale, and developer experience. Produce portability-focused documentation, operational runbooks, control evidence, and recommendations for production deployment across cloud environments.

Design and build scalable Python microservices and APIs for an AI-powered code modernization and migration platform. Develop MCP servers and agent tools, orchestrate agentic and multi-agent workflows, and manage backend service lifecycles. Ensure services are robust, observable, tested, and production-ready while shaping tool-calling, state-management, and distributed-system architecture. The role also involves Git workflow automation, developer tooling, and potentially containerized deployments and agent-facing interfaces.