Forward Deployed AI Engineer (AI)

TL;DR

Forward Deployed AI Engineer (AI): Embedding with B2B clients in private equity, lending, real estate, and SaaS to identify high-leverage AI opportunities and build production systems that deliver measurable business impact with an accent on LLM pipelines, agent orchestration, and fullstack deployment. Focus on architecting reliable RAG systems, conversational AI, document intelligence, and predictive analytics while converting proofs-of-concept into monitored production services.

In office, San Diego

Salary: $100,000 – $140,000 per year

Company

hirify.global is an AI strategy and integration firm that builds production AI systems for B2B companies in private equity, wholesale lending, real estate, and SaaS.

What you will do

• Embed with clients to map operations, identify AI use cases, run discovery sessions, translate business needs into technical architecture, and build end-to-end solutions.
• Architect and deploy LLM/RAG pipelines, agent systems, conversational AI, document intelligence, and predictive analytics using AI-native tools.
• Build evaluation frameworks, observability, data quality rules, ETL pipelines, and secure cloud deployments across AWS, GCP, and Azure.
• Present to C-suite, manage client relationships, and contribute to reusable playbooks, components, and AI transformation frameworks.
• Scout emerging models/tools, run experiments, and mentor junior engineers through pair programming and reviews.

Requirements

• 3+ years building software, with 2+ years focused on production AI/ML systems (not notebooks or proofs-of-concept).
• Shipped LLM-powered products to real users, hands-on with modern LLMs, RAG, agent frameworks, and prompt engineering.
• Fluent in Typescript & Python, comfortable across the stack (React/Next.js, PostgreSQL, cloud infrastructure).
• Exceptional communicator able to whiteboard architecture and present ROI to executives.
• AI-native workflow using tools like Claude Code, Cursor; strong judgment on prompt engineering vs. RAG vs. fine-tuning.
• Comfortable with ambiguity and client-facing delivery under pressure.

Nice to have

• Former founder, early startup engineer, or client-facing experience in consulting/professional services.
• Track record of AI systems impacting business KPIs in financial services, real estate, lending, or B2B SaaS.
• Led end-to-end launches of agent/LLM systems at scale, including monitoring and error handling.
• Active in AI community: writing, speaking, open source contributions.

Culture & Benefits

• Real ownership: scope, build, ship, and see direct impact without handoffs or bureaucracy.
• Variety across industries and problems with high velocity using AI-native culture and tools.
• Access to frontier models via partnerships with Anthropic, OpenAI, and YC.
• Bootstrapped, profitable company with upside sharing for early team members.
• Small pods, clear accountability, best idea wins.