Explore and experiment with emerging AI techniques to establish new and evolve existing enterprise AI patterns.
Design and run hypothesis-driven pilots, evaluating results, and translating learnings into clear recommendations, reference architectures and next steps.
Collaborate closely with AI platform and delivery teams to transition successful approaches into scalable architectures.
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.
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.

Requirements

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, timeboxing, 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.

Benefits

healthcare, dental, and vision insurance for employees and eligible dependents
several other wellness, financial, and work/lifestyle-specific benefits
401(k) retirement plan with a company match of 50% up to 12% of eligible compensation
generous PTO with a starting accrual of 22 days a year in addition to paid holidays and volunteer time

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

PythonAWS architectureSQLdata modelingAPIsretrieval-augmented generationtext-to-SQLLLM evaluationsagentic systemssoftware engineering

Soft Skills

communicationstorytellinginfluenceexperimentationdecision-making

Certifications

Bachelor’s degree in Computer ScienceBachelor’s degree in Engineering