AI Full Stack Engineer

We're partnering with JPMC to hire an experienced AI / ML Engineer with a strong full-stack Python background and proven expertise in building production-grade Agentic AI applications. This is an exciting opportunity to work on cutting-edge AI initiatives, designing and delivering intelligent agent workflows that are scalable, reliable, secure, and enterprise-ready. You'll be responsible for building sophisticated multi-agent systems, integrating LLM capabilities into business processes, and ensuring robust governance, observability, and performance across the AI stack., Design and develop multi-agent AI systems using frameworks such as Google ADK, LangChain, and LangGraph Build and maintain stateful workflows, orchestration layers, and agent decision-making processes Develop secure, scalable Python APIs and backend services that integrate with AI agents and enterprise systems Implement effective prompt engineering strategies, context management, memory handling, and system instructions Create reliable, structured outputs using JSON schemas and Pydantic validation Design and implement guardrails, fallback mechanisms, circuit breakers, and hallucination mitigation strategies Build observability frameworks, tracing tools, and evaluation-as-code capabilities to monitor agent behaviour and performance Collaborate with engineering and architecture teams to deploy AI solutions within cloud-native environments Ensure best practices across testing, security, scalability, and maintainability

Strong commercial experience in Python development, backend engineering, and distributed systems Experience building APIs, microservices, and scalable production applications Hands-on experience with LLM platforms including OpenAI, Gemini, Claude, or similar Proven experience with LangChain, LangGraph, Google ADK, or related AI orchestration frameworks Strong understanding of agentic architectures, workflow orchestration, and AI application design Experience working with Google Cloud Platform (GCP) Google Professional Cloud Architect Certification (mandatory) Knowledge of containerisation technologies and cloud deployments (Docker, Kubernetes, CI/CD pipelines) Strong testing mindset, including unit, integration, and automated testing approaches for AI-driven systems Ability to design resilient systems that manage asynchronous events, state transitions, and complex decision paths

Desirable

Experience implementing AI governance frameworks and responsible AI practices Exposure to observability tools, tracing frameworks, and AI evaluation platforms Experience working within large-scale enterprise environments, particularly financial services