Senior Applied AI Engineer

At Prologis, we don’t just lead the industry—we define it with a 1.3 billion square foot portfolio and an annual throughput of approximately $3.2 trillion. We create the intelligent infrastructure that powers global commerce, seamlessly connecting the digital and physical worlds. From agile supply chains to energy solutions, our ecosystems help your business move faster, operate smarter and grow sustainably. With unmatched scale, innovation and expertise, Prologis is a category of one—not just shaping the future of logistics but building what comes next.

Senior Applied AI Engineer

Prologis

In this role, you will explore and experiment with emerging AI techniques to establish new and evolve existing enterprise AI patterns. Your time will be spent designing and running hypothesis-driven pilots, evaluating results, and translating learnings into clear recommendations, reference architectures and next steps. You will collaborate closely with AI platform and delivery teams to transition successful approaches into scalable architectures.

Along the way, you’ll communicate findings through demos and concise readouts that connect technical outcomes to business impact.

• Drive AI innovation and rapid experimentation to advance Prologis capabilities (agentic workflows, reasoning approaches, evaluation methods, and emerging techniques).
• Design and execute timeboxed pilots with clear hypotheses, success metrics, and kill/scale decision points.
• Enhance and evolve existing enterprise AI patterns and standards (e.g., retrieval-augmented generation, text-to-SQL, evaluation/observability, guardrails) using new approaches and measured outcomes.
• Build reference implementations and handoff documents so successful experiments transition into scalable architectures in partnership with the Central AI team.
• Develop within the AWS ecosystem using secure, observable, cost-aware architectures and strong software engineering practices.
• Communicate results through demos and concise readouts that connect technical outcomes to business value, tradeoffs, and recommended next steps.
• Support priority project work as cycles allow, focused on de-risking and acceleration with clear entry/exit criteria.
• Continuously evaluate emerging AI tools, frameworks, and vendor offerings; synthesize external research, OSS trends, and vendor capabilities into actionable recommendations for Prologis.

• 8+ years of software engineering experience (or equivalent), delivering production-quality systems.
• Expert Python skills (clean architecture, testing, packaging, performance and reliability).
• Strong AWS architecture and development experience (security/IAM, networking, serverless and/or containers, monitoring/logging, cost controls).
• Strong data foundations: SQL, data modeling, APIs/integration patterns; comfortable incorporating RAG and text-to-SQL patterns into real solutions.
• Demonstrated rigor in experimentation: hypothesis-driven approach, evaluation plans, metrics, time boxing, and pragmatic decision-making.
• Ability to communicate clearly to both technical and business stakeholders; proven storytelling and influence through results.
• Bachelor’s degree in Computer Science/Engineering (or equivalent practical experience).
• Hands‑on experience designing and interpreting LLM evaluations (task success, faithfulness, hallucination analysis, cost/latency tradeoffs).
• Familiarity with modern LLM application stacks across vendors, with an ability to reason about abstraction tradeoffs, portability, and long‑term maintainability.
• Hands‑on experience with agentic systems (tool use, memory strategies, multi‑agent orchestration) and reliability/evaluation techniques.

• Experience with Dataiku (DSS), including building and operationalizing analytics/AI workflows; LLMOps experience is a plus.
• Experience building evaluation harnesses (golden sets, regression testing, error analysis) and partnering on risk/safety reviews.
• Exposure to OpenAI Agent Kit/Chat Kit and Apps SDK/MCP is a plus.
• Experience transitioning prototypes into enterprise‑ready patterns in partnership with platform and…