Senior Forward Deployed Engineer

TL;DR

Senior Forward Deployed Engineer (AI/LLM): Deliver hirify.global’s AI into enterprise grocery customers while building the platform that makes deployments fast, with an accent on production-grade LLM/agent systems, retrieval (RAG), and data pipelines. Focus on hardening field learnings into reusable knowledge/grounding layers, evals/tracing for quality (accuracy, hallucinations, latency, cost), and reliable serving infrastructure.

Location: Hybrid — San Francisco office (2 days/week). Remote work allowed only if residing in AL, AR, CA, CO, FL, GA, IL, KY, MA, MI, MT, MO, NV, NJ, NY, NC, OR, PA, TX, WA, UT, VA, WI.

Salary: $156,060 - $231,140 (U.S.)

Company

hirify.global builds an AI platform for grocery to reduce food waste and improve enterprise decision-making.

What you will do

• Scope and architect forward-deployed work with customer teams: data sources, architecture, and path to production.
• Embed with customer data/engineering teams (remote and on-site) and build production-grade pipelines and trustworthy data products from messy enterprise data.
• Design and ship LLM- and agent-powered systems (retrieval, agentic workflows, data-quality and analytics agents) reliable for production.
• Harden field solutions into the shared platform: knowledge/grounding layer (knowledge graph/ontology/retrieval), agent frameworks, and serving infrastructure.
• Build evals, tracing, and tooling to measure quality (accuracy, hallucination rate, latency, cost) and ship faster for the next customer.
• Own the flywheel so field learnings flow into the platform and platform improvements show up in subsequent deployments.

Requirements

• 3+ years building production software and data systems with strong production-grade code.
• Ability to take ambiguous problems and messy data landscapes, design a clean solution, and build it.
• AI/LLM depth: built real LLM/agent systems (e.g., retrieval/RAG, tool-use) and evaluated quality (not just demos).
• Data-engineering depth: build and operate data pipelines and work in modern cloud data platforms (e.g., Databricks, BigQuery, Snowflake).
• Comfort switching between forward-deployed customer work and building reusable platform infrastructure.
• Bias toward ownership and willingness to travel to customer sites regularly (~10–20%).

Nice to have

• Experience in grocery, retail, or supply chain data domains.
• Production knowledge graphs/ontologies/semantic layers; graph and vector stores and hybrid search.
• MCP or similar tool/context protocols; agent frameworks (e.g., LangGraph); LLM MLOps, model serving, and observability.
• Prior forward-deployed/implementation engineering experience or early-stage startup experience.

Culture & Benefits

• Comprehensive medical, dental, and vision coverage (majority of premiums covered), plus mental health support.
• Competitive base salary, meaningful equity (U.S. employees), and a 401(k) with generous company match.
• Flexible workspace support: home office stipend and coworking access via “Coworking Wallets”.
• Annual professional development budget and continuous learning culture.
• Monthly stipends for wellness/lifestyle (“Betterment”) and telecommunications.
• Flexible paid time off to recharge; full-time U.S. employees eligible.

Hiring process

• Interviews to assess technical depth across LLM/agent systems, data pipelines, and production evaluation.
• Discussion of forward-deployed vs platform-building fit and ownership expectations.
• Final steps to confirm role logistics (hybrid schedule and travel expectations).