Forward Deployed AI Engineer

Forward Deployed AI Engineer

Levi & Korsinsky, LLP

Location: New York, NY / United States

Work Arrangement: On-site

The Opportunity

Levi & Korsinsky, LLP is a national law firm with a growing consumer practice focused on class actions, data breach and privacy matters, mass arbitrations, and other complex litigation. Our teams combine sophisticated legal strategy, technology, data, and operational discipline to pursue meaningful results for our clients.

We are building internal AI agents, automation systems, and software directly around real operational problems inside a fast-moving law firm.

Ideas often start with a simple observation from leadership — something is inefficient, information is missing, a workflow is stuck, or an activity should work differently. We rapidly prototype solutions, learn from how they perform in the real environment, and iterate.

We are seeking a Forward Deployed AI Engineer to work directly alongside leadership and internal teams, understand these problems as they emerge, and take the most valuable ideas from early prototype to robust, integrated production systems.

This is a highly hands-on role for someone who is equally comfortable understanding a business workflow, inspecting an existing prototype, designing the architecture, and writing the code required to make it work.

What You’ll Do

• Embed closely with leadership and operational teams to understand how the business actually works and where technology can create leverage.
• Take ambiguous ideas and operational problems from discovery through prototype, deployment, and iteration.
• Evaluate existing AI tools, dashboards, automations, and prototypes and determine how they can be improved, hardened, or expanded.
• Design and build production-ready AI agents and full-stack applications.
• Build agents capable of reasoning across workflows, using tools, monitoring processes, identifying exceptions, and escalating decisions to humans when appropriate.
• Integrate AI systems with existing applications, APIs, databases, case-management systems, and internal workflows.
• Improve reliability, performance, security, observability, and maintainability of systems already in use.
• Identify where a prototype should remain lightweight versus where it requires deeper engineering investment.
• Turn successful approaches into reusable components, patterns, and infrastructure for future AI initiatives.
• Work with other engineers when needed while remaining personally capable of building and debugging critical parts of the system.
• Explain technical limitations, opportunities, and trade-offs clearly to non-technical leadership.

What You Bring

• 5+ years of strong software engineering or technical product-development experience.
• Strong full-stack engineering skills using Python, JavaScript/TypeScript, or comparable technologies.
• Experience designing, building, and deploying production applications end-to-end.
• Hands-on experience with LLM-powered applications, AI agents, tool calling, RAG, workflow orchestration, or comparable generative AI systems.
• Strong understanding of APIs, databases, integrations, cloud infrastructure, authentication, and production system design.
• Ability to operate effectively when requirements are incomplete and evolve through experimentation.
• Strong product and systems thinking — you understand not only how to build something, but why it should exist and how it fits into a larger workflow.
• Ability to move quickly without losing engineering judgment.
• Strong communication skills with both technical and non-technical stakeholders.
• High ownership mindset with the ability to stay with a problem from initial idea through production and continuous improvement.

You Might Thrive Here If You

• Prefer solving real operational problems over working from perfectly defined tickets.
• Enjoy sitting close to users and decision-makers.
• Naturally ask, “How far can we take this?” after the first version works.
• Can rapidly prototype while also knowing when a system needs proper engineering rigor.
• Enjoy understanding unfamiliar systems, workflows, and business domains.
• Are comfortable challenging assumptions and proposing better approaches.
• Like building systems that increasingly allow AI agents to perform meaningful work while humans supervise important decisions.

What Success Looks Like

• Valuable prototypes no longer stop at V1.
• Existing AI systems become measurable…