Software Engineer - Applied AI / Product Engineering
- Compensation
- Not disclosed
- Location
- New York, New York, United States
- Work setup
- Not specified
- Seniority
- Not specified
- Employment
- Full time
- Experience
- Not specified
- Category
- Ai engineering
- Posted
Skills
Must have
- Proven AI Engineering Experience: Demonstrated track record of shipping production-level software with autonomous LLM agents or workflows at its core.
- Full-Stack / Product Mindset: Ability to own features end-to-end without relying on static product specs or partial hand-offs.
- Technical Rigor & System Design: Ability to defend technical design choices around context management, system evaluation, framework choices, and agent harnesses.
- Strong AI Curiosity: Proactive approach to testing new models, agentic frameworks, and AI tools as they are released.
- Fast Learning & Domain Synthesis: Capability to rapidly absorb complex domain expertise (e.g., financial underwriting or commercial real estate logic) and translate it into technical specifications.
- On-Site Availability: Ability to work full-time on-site, five days a week, at the SoHo, NYC office.
Full description
Location: New York, United States (Office) - NYC - SoHo Office
On-Site | Full-timeCompensation: $160K - $250K
We are hiring on behalf of our client, a high-growth, Series A-backed artificial intelligence company operating in the commercial real estate (CRE) sector who is seeking an experienced Software Engineer – Applied AI / Product Engineering to join its engineering team in New York City.
Our client delivers an enterprise-grade AI platform that automates complex commercial real estate workflows, integrating email, financial models, presentation decks, and property databases into a unified, intelligent system. Having already secured enterprise multi-year contracts with top global commercial brokerages, the organization is scaling rapidly to meet significant market demand.
This role focuses on designing, building, and deploying AI-driven products and agentic systems. The ideal candidate thrives in a fast-paced environment, takes full end-to-end ownership of features, and continuously explores evolving AI paradigms to build robust, practical solutions for enterprise users.
Key Responsibilities
- Product & AI Engineering: Design, build, and deploy agent-centric software architectures and end-to-end AI workflows beyond basic conversational interfaces.
- End-to-End Feature Ownership: Manage the full product development lifecycle—from sitting directly with end-users to understand core pain points, through architectural design, implementation, and deployment.
- Agent Evaluation & Harnessing: Develop, maintain, and defend robust evaluation frameworks, harness selections, and context management strategies (context window optimization, filtering, and retrieval).
- Domain Knowledge Extraction: Collaborate directly with industry domain experts to distill complex workflow logic into structured system requirements, prompts, and evaluation metrics.
- Continuous Paradigm Evaluation: Actively benchmark, test, and re-evaluate emerging LLM models, frameworks, and agent paradigms to continuously refine and improve system performance.
Requirements
- Proven AI Engineering Experience: Demonstrated track record of shipping production-level software with autonomous LLM agents or workflows at its core.
- Full-Stack / Product Mindset: Ability to own features end-to-end without relying on static product specs or partial hand-offs.
- Technical Rigor & System Design: Ability to defend technical design choices around context management, system evaluation, framework choices, and agent harnesses.
- Strong AI Curiosity: Proactive approach to testing new models, agentic frameworks, and AI tools as they are released.