How to apply for Forward Deployed AI Engineer (Senior)

About AZX, PBC.

AZX is a public benefit corporation founded in 2024 that is already profitable and works with category leaders like CBRE, LevelTen Energy, Flexe, and utilities. The company focuses on AI transformation in critical industries such as clean energy, decarbonization, climate risk, energy systems, and global economics. It aims to be a long-term place to work for people who care about both AI and positive impact.

About the role

As a forward deployed AI engineer, you will scope, build, deploy, and measure AI systems inside client environments such as utilities, commercial real estate, and logistics. Your software runs in the client's cloud, under their identity provider and toolchain, inside their compliance framework, and integrates with their systems of record. You will work on agentic AI with a correctness envelope, including document intelligence with deterministic validation, voice-of-customer AI, cognitive digital twins, and human-in-the-loop workflows, all measured against real before/after baselines.

A typical day

You might spend the morning with a client's operators to understand a workflow and define success metrics, then work on integrating an AI system into their cloud and systems of record. Later, you could test the system against real data, refine the mix of models and tools, and prepare to measure before/after impact. The exact daily routine is not specified, so ask about team structure and typical project cycles during interviews.

Who AZX, PBC. is looking for

• Experience building and deploying AI systems in client environments, not just in a lab or internal sandbox.
• Comfortable working with cloud infrastructure, identity providers, and compliance frameworks, and integrating with systems of record.
• Able to sit with client operators to discover requirements and define problems, rather than waiting for a finished spec.
• Skilled at selecting the right mix of models and tools for the job, with a focus on correctness, auditability, and measurable outcomes.

Tips for this application

• Highlight any experience deploying AI in regulated or high-stakes environments like utilities, real estate, logistics, or energy.
• Show that you can work directly with clients to scope problems, not just execute a given spec.
• Demonstrate familiarity with agentic AI, document intelligence, or human-in-the-loop workflows in your resume or portfolio.
• Mention specific examples of AI systems you built that had measurable before/after impact.
• Explain why you want to work at a public benefit corporation focused on clean energy and climate risk, and how that connects to your past work.

What to cover in your cover letter

['Your experience deploying AI inside client environments, including cloud, identity, and compliance constraints.', 'A concrete example of scoping and building an AI system with a client, and how you measured its impact.', 'Your technical approach to agentic AI with correctness and auditability, especially in document intelligence or similar areas.', 'Why you want to work on AI for clean energy, decarbonization, and climate risk at a public benefit corporation.']

Research before applying

• Study AZX's clients (CBRE, LevelTen Energy, Flexe) and the industries they operate in: real estate, energy, logistics, and utilities.
• Understand what a public benefit corporation is and how AZX's mission in clean energy and climate risk might shape its work.
• Look into the technical meaning of 'agentic AI with a correctness envelope' and 'cognitive digital twins' to speak about them in interviews.
• Check AZX's founding date (2024) and profitability from inception to understand the company's stage and stability.

Likely interview topics

Based on the job description, expect questions about:

• How you would approach scoping an AI project with a utility or commercial real estate client who has no finished spec.
• Your experience with agentic AI and how you ensure correctness and auditability in high-stakes workflows.
• How you handle deploying software in a client's cloud under their identity provider and compliance framework.
• A time you integrated AI with a client's systems of record and measured before/after baselines.
• Your familiarity with document intelligence, voice-of-customer AI, or cognitive digital twins.

Common mistakes to avoid

• Focusing only on model building and ignoring deployment, integration, and compliance aspects of the role.
• Giving generic answers about wanting to work in AI without connecting to AZX's specific industries or public benefit mission.
• Failing to show how you work directly with clients to discover requirements and measure impact.