AI Applied Engineer

Applied Value Group; New York (NY); On-site; USD 120,000 - 170,000; Full time

Job summary

Applied Value Group is seeking an AI Applied Engineer to build tools that put our expertise into production. You will create AI agents and modular skills connected to the firm’s knowledge base, turning domain expertise into capabilities that consultants can assemble into workflows.

This builder role partners with the Director of AI and with consultants to move from surfacing information to actionable tools that practitioners and clients actually use.

Qualifications

• 3–6 years of software engineering experience, with at least 1 year shipping AI/ML or LLM features.
• Strong Python or TypeScript fundamentals and familiarity with the modern web stack (APIs, light frontend, data plumbing).
• Practical experience with LLM tooling (prompt engineering, retrieval, agent frameworks, evals).
• Bias to ship: pragmatic tradeoffs to get a useful tool in front of a real user and iterate.

Responsibilities

• Build AI tools end to end: prompts, retrieval, orchestration, UI, and glue code for consultant workflows.
• Work directly with consultants, partners, and domain experts to learn the methodology and ship it.
• Build on the firm’s governed stack and improve patterns as they evolve.
• Harden prototypes into internal tools with code review, error handling, and observability.
• Contribute to shared infrastructure that teams reuse instead of one-off scripts.
• Run evals on what you ship and feed learnings into the next iteration.
• Help consultants build by pairing with them and pointing to reusable parts of the stack.

Skills

Python

TypeScript

APIs

LLM tooling

Prompt engineering

Job description

Applied Value’s mission is unchanged: advise clients on their most important decisions and provide the hands‑on support to act on them. AI is reshaping how consulting gets done, but paired with human judgment it is a force multiplier, not a replacement. The consulting firms that win the next decade will supercharge their strategy and delivery with AI. As organizations run leaner, demand grows for operator‑led teams that can land, diagnose, build, and hand back a working solution on the latest technology.

The AI Applied Engineer builds the tools that put AV’s expertise into production. You will build AI agents and modular skills that connect to the firm’s knowledge base, turning AV’s domain expertise and operator‑grade analytics into capabilities any consultant can run and compose into larger workflows. This is where AI becomes real leverage for the firm and its clients.

This is a builder role rather than a research role. You will partner with the Director of AI on what gets built and the quality bar, and with consultants, partners, and domain experts on the methodology. The goal is to move work past surfacing information and into acting on it, shipping tools on the firm’s stack that consultants and clients actually use.

Key responsibilities

• Build AI tools end to end: prompts, retrieval, orchestration, UI, and the glue code that makes them usable in a consultant's workflow.
• Work directly with consultants, partners, and domain experts. Sit in the room, learn the methodology, and turn it into something that ships.
• Build on the firm's governed stack. Use the established patterns and improve them as they evolve.
• Harden consultant prototypes into internal tools others can reuse: code review, error handling, observability, and documentation.
• Contribute to shared infrastructure that teams reuse, rather than one‑off scripts.
• Run evals on what you ship. Check whether the output is good enough and where it breaks, then feed that into the next iteration.
• Help consultants build. Pair with them, review their prototypes, and point them to reusable parts of the stack.

What we’re looking for

• 3–6 years of software engineering, including at least one year shipping AI/ML or LLM features.
• Strong Python or TypeScript fundamentals and comfort across the modern web stack (APIs, light frontend, data plumbing).
• Practical experience with LLM tooling (prompt engineering, retrieval, agent frameworks, evals) and a sense for when a tool is good enough to ship.
• A bias to ship. You make pragmatic tradeoffs to get a useful tool in front of a real user, then iterate.
• Strong communication. You can sit with a non‑technical consultant or partner, understand what they need, and turn it into a build plan.
• Curiosity about how the work gets done: the methodology, the business logic, and what good looks like.

Nice to have

• Experience in consulting, professional services, or applied research where the user is non‑technical and the problem is loosely defined.
• Familiarity with enterprise knowledge platforms, Office add‑in development, or the integrations that consulting tools tend to need.
• Side projects, open‑source work, or shipped prototypes.
• Experience with evals and observability that turn a prototype into a tool you can leave with a client.

Direct line to real users: the consultants, partners, and clients who use what you build every day.

Wide scope. Every part of the consulting business is a candidate for AI, and the firm is committed to using it.

A real career path, deeper in engineering toward staff or principal, or into product and applied‑AI leadership.