Senior AI Applied Engineer

Posted One Month Ago
Be an Early Applicant
5 Locations
In-Office
125K-186K Annually
Senior level
Information Technology • Logistics • Real Estate • Energy
The Role
Leads enterprise AI innovation through hypothesis-driven pilots, evaluation, and experimentation with agentic workflows, RAG, text-to-SQL, and emerging LLM techniques. Designs secure, observable, cost-aware AWS architectures, develops reference implementations, and transitions successful prototypes into scalable enterprise patterns. Communicates technical findings, business impact, tradeoffs, and recommendations while evaluating AI tools, frameworks, vendors, and research.
Summary Generated by Built In
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.

Job Title:

Senior AI Applied Engineer

Company:

Prologis

A day in the life

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.

Key Responsibilities:

• 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.

Required Qualifications:

• 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.

Preferred Qualifications:

• 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 AgentKit/ChatKit and Apps SDK/MCP is a plus.

• Experience transitioning prototypes into enterprise-ready patterns in partnership with platform and delivery teams.

Hiring Salary Range of: $125,000 - 186,000. Salary and whole compensation package (bonus target) to be determined by the candidate’s location, education, experience, knowledge, skills, and abilities, as well as internal equity and alignment with market data.

#LI-TA1

People First

Each of us working at Prologis plays an essential role in the enduring success of our company. We value people who are decisive, courageous and adaptable. While we are one company, locations and departments operate with autonomy and accountability. Individuals take the initiative here.

When you join Prologis, you work shoulder to shoulder with some of the top talent in the industry to do the best work of your career. Every employee belongs. Every employee contributes. Employees advance their careers here.

As a successful global enterprise, Prologis has never lost sight of what matters most, our strong belief that our people are the most important part of our business. And because of that, we provide a generous total rewards package and take a lot of time to focus on quality management and leadership development. People come first here.

All full-time roles in the US come with a robust benefits package which includes healthcare, dental, and vision insurance for employees and eligible dependents. Prologis also offers several other wellness, financial, and work/lifestyle-specific benefits. Our 401(k) retirement plan has a company match of 50% up to 12% of eligible compensation. We also offer generous PTO with a starting accrual of 22 days a year in addition to paid holidays and volunteer time. 

All job offers are contingent upon successful completion of background verification. Prologis is an Equal Opportunity/Affirmative Action employer and all qualified applicants will receive consideration for employment without regard to race, color, religions, sex, national origin, sexual orientation, gender identity, disability status, protected veteran status, or any other characteristic protected by law.

Employment Type:

Full time

Location:

San Francisco, California

Additional Locations:

Chicago, Illinois, Denver, Colorado, Los Angeles, California, Phoenix, Arizona

Skills Required

  • 8+ years of software engineering experience or equivalent, delivering production-quality systems
  • Expert Python skills, including clean architecture, testing, packaging, performance, and reliability
  • Strong AWS architecture and development experience, including security/IAM, networking, serverless or containers, monitoring/logging, and cost controls
  • Strong data foundations, including SQL, data modeling, APIs, and integration patterns
  • Experience incorporating retrieval-augmented generation and text-to-SQL patterns into real solutions
  • Demonstrated rigor in experimentation, including hypotheses, evaluation plans, metrics, timeboxing, and decision-making
  • Ability to communicate clearly with technical and business stakeholders and influence through results
  • Bachelor's degree in Computer Science or Engineering, or equivalent practical experience
  • Hands-on experience designing and interpreting LLM evaluations, including task success, faithfulness, hallucination analysis, and cost/latency tradeoffs
  • Familiarity with modern LLM application stacks across vendors and abstraction, portability, and maintainability tradeoffs
  • Hands-on experience with agentic systems, including tool use, memory strategies, multi-agent orchestration, and reliability/evaluation techniques
  • Experience with Dataiku DSS, including building and operationalizing analytics or AI workflows
  • LLMOps experience
  • Experience building evaluation harnesses, golden sets, regression testing, error analysis, and partnering on risk or safety reviews
  • Exposure to OpenAI AgentKit, ChatKit, Apps SDK, or MCP
  • Experience transitioning prototypes into enterprise-ready patterns with platform and delivery teams

Prologis Compensation & Benefits Highlights

The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Prologis and has not been reviewed or approved by Prologis.

  • Fair & Transparent Compensation — Pay is considered competitive for many roles, with frequent references to fair pay for the job and a pay-for-performance philosophy. Feedback suggests compensation sentiment tends to be stronger than categories like advancement or management.
  • Leave & Time Off Breadth — Time off and PTO are described as strong, with some accounts calling them amazing. Feedback suggests paid time off is a standout element of the overall package.
  • Healthcare Strength — Healthcare and core benefits are portrayed as comprehensive and part of a robust total rewards package. Feedback suggests medical, dental, and vision coverage are notable strengths.

Prologis Insights

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The Company
HQ: San Francisco, CA
1,700 Employees
Year Founded: 1983

What We Do

Prologis is the global leader in logistics real estate. In partnership with top manufacturing and distribution companies (e.g., Amazon, BMW, DHL, FedEx, Pepsi), we ensure timely delivery of the products that make modern life possible.

Why Work With Us

We embrace change. We ask “why” and trust that discomfort and failure are part of success. We listen, question, then commit to each other. Together as one team, we actively seek and leverage different perspectives, challenge our own best ideas and commit to a common goal. We simplify and sprint. Speed and agility drive results.

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