Python & Java Full Stack Developer (AI-Assisted)

At Jabil (NYSE: JBL), we are proud to be a trusted partner for the world's top brands, offering comprehensive engineering, supply chain, and manufacturing solutions. With 60 years of experience across industries and a vast network of over 100 sites worldwide, Jabil combines global reach with local expertise to deliver both scalable and customized solutions. Our commitment extends beyond business success as we strive to build sustainable processes that minimize environmental impact and foster vibrant and diverse communities around the globe. How will you make an impact? As the Python & Java Full Stack Developer (AI-Assisted), you will build AI-assisted platforms for manufacturing tests and debug automation software with direct, real-world consequences on the factory floor. You will own end-to-end features across event-driven platforms, from cloud infrastructure and backend microservices, up through an agentic LLM pipeline, out to a modern web frontend. LLMs are a core building block of what we build, and AI coding agents are a core part of how we build it but we treat them as power tools operated by strong engineers, not autopilots. This role requires someone who can define work with precision, critically evaluate agent-generated output, and write code themselves when needed. As the Python & Java Full Stack Developer (AI-Assisted), you are equally comfortable writing a Python Lambda handler, designing an agent tool, building a Java microservice, defining infrastructure as code, tuning a message-bus consumer, and building an Angular component with an AI agent working alongside you. How We Work with AI We use AI coding agents Claude, Kiro, and similar agentic IDEs/CLIs (we use Kiro) but as power tools in the hands of engineers who could do the work without them. We are seeking a passionate developer who has experienced building without AI, understands its capabilities and limitations, and knows when to rely on AI versus writing code themselves. The challenge isn’t generating code. It’s defining requirements precisely and rigorously validating the output. Engineering Fundamentals You can read, debug, and correct generated code as readily as you can produce it, and you can justify an architectural decision independent of what any tool suggested. Spec-Driven Development Decompose features into clear requirements, design, and task breakdowns that both humans and agents execute against, treating the spec as the source of truth and iterating on it. Skills and Powers Author and use reusable agent capabilities (skills, steering docs, custom tools/MCP integrations, project conventions) to encode team standards and make agents productive in a large codebase. Prompt and Context Engineering Give agents the right context (conventions, contracts, constraints) and validate their output rather than trusting it blindly. Correctness Discipline Pair agent velocity with strong testing, review, and observability so fast-moving changes stay safe in production. What will you do? Design, build, and ship feature end-to-end across the full stack AWS infrastructure, Python and Java backend services, agentic LLM workflows, and the Angular frontend for a system serving manufacturing across multiple sites. Build and evolve agentic AI workflows on Amazon Bedrock. (LLM reasoning + embeddings): tool/function calling, RAG, prompts, validation loops, and fallback strategies that stay correct and observable under production load Own backend services across two ecosystems Python serverless (Lambda, event-driven) and Java microservices (Spring Boot/Quarkus) with REST and event-driven APIs, persistence, async pipelines, and message-bus integrations Care for idempotency, retries, and dead-letter handling throughout Define and evolve infrastructure as code (AWS CDK) across multi-stack deployments with cross-stack references. Treat published API surfaces as backwards-compatibility contracts, using shared interface/schema libraries consumed across services and…

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