Forward Deployed Engineer, Private Markets

Cedar Peak Partners; New York (NY); On-site; USD 180,000 - 250,000; Full time

Job summary

Cedar Peak Partners is seeking an engineer to build AI-powered tools for investment teams, sitting with users to understand workflows and deliver end-to-end improvements. You will own architecture, data integration, and deployment, partnering with a business-side AI counterpart from discovery to adoption.

The role is based in New York City, with a focus on enterprise data and regulated environments benefiting from robust security and auditing.

Qualifications

• Production engineering experience with end-to-end system ownership.
• Experience building LLM-based applications, agents, or orchestration.
• Ability to work with ambiguity and deliver concrete solutions.

Responsibilities

• Build production AI applications and enterprise-grade data pipelines.
• Implement robust APIs, cloud services, and security.
• Collaborate with business AI counterpart from discovery to adoption.
• Measure impact: accuracy, adoption, time savings, and risk.

Skills

Python

TypeScript

APIs

Cloud

Distributed systems

LLM apps

Data pipelines

Tools

OpenAI API

Pandas

Job description

Our client, a global alternative asset manager, investing across private equity, credit, infrastructure and real estate. It is putting AI into the daily work of its investment teams, and this is the engineering seat that builds it.

The seat

This is a forward deployed seat. Your users are internal: the people evaluating deals and running portfolios. You sit with them, watch how the work actually gets done, and find where fragmented data, manual handoffs and repetitive steps slow down the decisions that matter. Then you own the fix end to end, from architecture and code through data integration, evaluation, deployment and the adoption that makes it stick. You are paired with a business-side AI counterpart and you are both in the room from the first discovery conversation to the measurement afterwards.

What you will build

• Production AI applications and agents for investment, credit and corporate teams
• The plumbing that makes enterprise data usable: pipelines, retrieval, and interfaces that hold up on imperfect data
• Reusable skills, tools, prompts, connectors and workflow components, so the second team gets it faster than the first
• Evaluation that means something: accuracy, reliability, adoption, time saved, decision quality, risk
• The judgment on each workflow, deciding where AI belongs, where ordinary software is better, and where a human stays in the loop

What you bring

• A real engineering foundation: production systems you designed, built and operated
• Python or TypeScript, with APIs, cloud, distributed systems and enterprise integration behind you
• LLM applications shipped: agents, RAG, tool use, orchestration, and the evals to know whether they work
• Data judgment: pipelines, databases, retrieval, and making messy enterprise data usable
• Comfort in ambiguity: take a vague problem, ask the questions users cannot articulate, and converge on something concrete
• Standards that survive speed: testing, observability, security, privacy
• Commercial curiosity about how investments get evaluated and decisions get made

It would be beneficial if you have built AI systems in regulated environments. This role is based in New York City.