About SaxeCap: SaxeCap is the premier AI transformation and investment platform in private equity. We created the concept of buying out traditional businesses and transforming them with AI in 2018, and today we are the go-to AI transformation partner for 60+ PE firms globally, including 9 of the top 10 by AUM and the world's leading sovereign wealth funds. We've transformed 140+ businesses across healthcare services, business services, financial services, software, consumer, and more, driving billions of dollars of enterprise value expansion along the way. We operate at the intersection of three businesses: a private equity investment arm, an in-house AI transformation firm that designs and deploys bespoke GenAI, ML, and automation solutions, and a suite of proprietary software products. Our team comes from places like Google, Palantir, Meta, Surge AI, Blackstone, TPG, Cerberus, Millenium, Balyasny, McKinsey, Bain, Oliver Wyman, MIT, Stanford, GSB, HBS, and Wharton. Our leadership includes early investors in OpenAI, Anthropic, Groq, SpaceX, Neuralink, Mercor, Lambda Labs, etc. But what makes our team exceptional isn't where they came from or what they've invested in, it's what they ship. We are builders, not slide-makers.
About the Role:
We are hiring a Forward Deployed AI Engineer to join our AI transformation team. This is not a typical software engineering role. You will not be maintaining a single codebase or optimizing one product. You will be building and deploying AI solutions across dozens of companies, industries, and problem domains, working directly with clients and PE sponsors to solve their highest-value business problems with production-grade GenAI, ML, and automation. In a given month, you might build a RAG system that automates a core operational workflow for a healthcare services company, deploy an ML model that optimizes pricing for a financial services platform, architect a data pipeline that unlocks analytics capabilities for a business that has never had them, and mentor junior engineers on the team through all of it. Every solution you build is tied to measurable business impact. You will see your code move EBITDA, not just pass tests. This role sits at the intersection of engineering excellence and client impact. You will be in the room with executives and PE partners, translating business problems into technical architectures, then building and shipping the solutions yourself. If you want to be the best AI engineer in the world, there is no faster way to get reps across industries, problem types, and AI techniques than this role.
About You:
You are an engineer who builds fast, ships to production, and cares about business outcomes, not just technical elegance. You are comfortable working directly with non-technical stakeholders, translating ambiguous business problems into clean technical solutions, and iterating in tight cycles. You take pride in code that works in the real world, not just in a notebook. Specifically:3+ years of experience building and deploying software products end to end (design, implementation, testing, deployment, maintenance) Expert in Python (Django, Flask, FastAPI, Pandas, etc.) and/or JavaScript (React, Node). These are our primary languages and you should be deeply fluent in at least one. Strong experience with SQL, data modeling, and cloud infrastructure (AWS, GCP, or Azure) Hands-on experience with generative AI: you have built RAG systems, AI agents, prompt engineering pipelines, fine-tuned models, or similar. This is not a "nice to have," it is core to the role. Experience with data engineering, integrations, ETL/ELT pipelines, and working with messy, real-world data Experience with data science and ML: NLP, recommender systems, classification, regression, neural networks. You don't need to be a research scientist, but you should be comfortable selecting, training, and deploying models. You have owned production systems. You know what it means to deploy, monitor, debug, and maintain software that real users depend on. You are comfortable in client-facing environments. You can explain technical trade-offs to a CEO, scope a solution with a business stakeholder, and present a demo to a PE partner. Experience managing or mentoring other engineers is a strong differentiator. You will be expected to elevate the technical bar of the broader team. B.S. or M.S. in Computer Science, Engineering, or a related technical field Legally authorized to work in the USWhat Sets the Best Candidates Apart: The engineers who thrive at SaxeCap are a specific breed. They combine deep technical skill with business intuition and client presence. The strongest signals we look for: Production over theory. You have shipped AI systems that real users and real businesses depend on. Kaggle competitions and research papers are fine, but we care most about code running in production with measurable impact. Breadth and speed. You can pick up a new domain, a new tech stack, or a new problem type and be productive in days, not months. You've worked across multiple problem areas and can context-switch without losing quality. Full-stack AI fluency. You don't just build models. You build the data pipelines that feed them, the APIs that serve them, the frontends that expose them, and the monitoring that keeps them running. You think in systems, not components. Client-facing instinct. You can sit in a room with a CFO or a PE partner, understand what they actually need (not just what they say they need), and translate that into a technical plan on the spot. The best FDAIs at SaxeCap are as comfortable in a boardroom as they are in a terminal. Builder DNA. Open-source contributions, side projects, hackathon wins, or a startup attempt. You build things because you can't help it. Compensation & Growth: SaxeCap offers highly competitive compensation, and you will work directly with our senior leadership from day one, with significant client exposure and rapid professional development. The team is lean by design, there are no layers between you and impact. You will get more reps across AI techniques, industries, and business problems in one year at SaxeCap than in five years at a typical tech company.
