Applied AI Engineer

Career Renew is recruiting for one of its clients an Applied AI Engineer - this is a hybrid position in New York. Salary range: 200-250K USD plus benefits plus equity.

We are building the AI platform for hedge funds and asset managers. We deploy software and custom workflows that make funds AI-native - connecting their data, encoding their expertise into agents and skills, and automating their highest-value workflows. The firms that are winning with AI aren't using better models. They have a more thoughtful ecosystem around them, and that's what we build.


Unlike most AI startups, we're cash-flow positive and client-funded - our growth comes from the trust of our clients, the value we deliver, and the exceptional caliber of our team.


Our team combines engineers from top institutions across finance (Citadel, Goldman Sachs, Millennium, D.E. Shaw, Two Sigma, and Bridgewater) and technology (leading data and AI startups) with forward-deployed specialists who embed directly with clients to automate real workflows. We sit at the intersection of deep finance domain knowledge and cutting-edge AI engineering - closing the gap between what AI can do and what funds are getting out of it.


Role Overview

We're looking for software engineers to chart the course of how AI is reshaping institutional finance. You'll build AI Infrastructure (observability, agent orchestration, expert skills, tools as CLI's and MCP, data orchestration, and UI component libraries) that are leveraged by some of the world's most sophisticated hedge funds as part of their AI implementations, working directly with their investment teams to turn complex workflows into elegant, production-grade applications.


This role sits at the intersection of AI implementation and financial software. You won't just use AI tools - you'll build AI-powered features directly into client platforms: LLM-driven research intelligence, agentic workflows, MCP-connected data sources, and automation layers that compress weeks of analyst work into seconds. The ideal candidate is a strong full-stack engineer who is fluent in modern AI tooling and deeply curious about how hedge funds and asset managers think, invest, and operate.


Speed is a core part of the job. Our model is to deliver fully customized platforms in weeks, not months, which means you need to ship with conviction, iterate based on real user feedback, and know when to build from scratch versus leverage proven infrastructure.


Key Responsibilities

• AI-Powered Feature Development: Build LLM-powered features directly into client-facing platforms, including research intelligence tools, natural language query layers, automated summarization, and agentic workflows that fundamentally change how investment teams work
• Agentic Tooling & MCP Integration: Design and implement MCP-connected data sources, agentic pipelines, and AI orchestration layers using frameworks like Claude Code, LangGraph, Open Claw, Open Code and similar, extending client platforms with live, intelligent data access
• Full-Stack Application Development: Build end-to-end applications tailored to each client's unique portfolio analytics, risk management, and research workflows-from backend APIs to responsive frontends
• Backend Services: Design and maintain high-performance APIs using Python (FastAPI or similar) that power client-specific data access, analytics, and AI inference
• Frontend Development: Build intuitive, responsive user interfaces in React that enable investment teams to interact with complex financial data clearly and efficiently
• Data Pipeline Development: Build and maintain ETL pipelines that handle critical financial market data-positions, securities, risk metrics, and research signals-with reliability and performance
• Financial Analytics: Implement analytics layers for performance and risk calculations using timeseries and linear algebra operations (Pandas, Polars)
• Ship Fast, Iterate Often: Deliver working software in compressed timelines, gather direct feedback from hedge fund users, and continuously improve, treating speed and quality as complementary, not competing
• Kubernetes Deployments: Be able to work fluidly with Kubernetes within each client environment to be able to ship fast and reliable.

Requirements :


3 - 8 years of experience as a full-stack SWE or applied AI engineer (institutional investor or fintech) (Mandatory)

Demonstrated record using Agentic AI tooling effectively (e.g., Claude Code, Codex, MCP servers) and experience building user-facing products from 0-to-1 (Mandatory)

Relevant institutional investor and/or fintech experience (e.g., Two Signma, DE Shaw, Citadel, P72, Addepar) or other data-first and quantitative fields (e.g., health/bio tech) (Mandatory)

AI implementation experience inc. hands on building with LLM APIs, MCP servers, agentic frameworks (Claude Code, OpenClaw, LangChain), prompt engineering etc (Nice-to-have)

Strong technical fundamentals including expertise in Python (non-negotiable; API experience using FastAPI, Flask, or Django etc highly preferred) (Mandatory)

Effective in unstructured environments and can solve loosely defined problems (Mandatory)

Genuine conviction that AI is transforming software (active use of AI tools) and deep interest in finance (how institutional investors think, make decisions, and use tech) (Mandatory)

Why you should join us

• Bootstrapped, Post-PMF, and Built to Last - We've reached $10M+ ARR with a lean team and no external funding - a strong signal of genuine product-market fit built on real revenue, not VC runway. There are no externally imposed milestones or investor pressure dictating the roadmap. For candidates who want to join at the most exciting inflection point - where the product has proven itself but the scaling is still ahead - this is a rare opportunity to have outsized impact and grow alongside the business as it enters its next phase.
• R&D Culture Within a Commercial Business - The company operates like a hybrid between a SaaS startup and an R&D team - constantly evaluating new developments in AI and accommodating the roadmap accordingly. Engineers are not just building towards a fixed vision; they are actively contributing to how that vision evolves. The culture values curiosity, craft, and elegant solutions to genuinely hard problems.
• Cutting Edge by Necessity - Because our product is built on top of rapidly evolving AI infrastructure, engineers are working at the frontier of what is possible - integrating LLMs, agentic workflows, and MCP-connected intelligence into real client platforms. Work ships into production and directly impacts how some of the world's most sophisticated hedge funds make investment decisions.
• Technology as the End, Not the Means - For candidates coming from hedge funds or finance, we offer the quantitative, intellectually stimulating problem-solving they enjoy - without the backseat dynamic relative to portfolio managers and traders. Here, technology is the revenue-generating core of the business, and the mission is to redefine how it powers institutional investing.
• Genuine Ownership at Every Level - With a lean team and no layers of bureaucracy, individuals at all levels are trusted to drive decisions and outcomes. There is limited top-down direction, and the scope of responsibility is meaningfully broader than what is typically available at larger or more structured organisations.
• Calibre of the Team - The engineering team brings experience from some of the most demanding institutions in finance and technology - Citadel, Goldman Sachs, Millennium, and D.E. Shaw. For candidates who care about the quality of the people they work alongside and who they can learn from, this is a meaningful signal.
• 0 to 1, Every Time - This is a 0 to 1 role in the truest sense. Every project involves taking a problem from first principles and turning it into a working product. There is no legacy codebase to maintain, no internal tooling, and no narrow feature lane - the applications being built, from agent health scoring to AI-powered handoff documents to orchestrator systems, are problems that have not been solved before.