Forward Deployed Engineer

Overview

LLR Partners is hiring two Forward Deployed Engineers to build the AI-native products that generate real operating leverage across the firm more AUM per head more decisions per hour more institutional memory retained in the firm. You will work shoulder-to-shoulder with LLRs teams starting with Investment Origination and the Value Creation Team and expanding across every function to ship agentic workflows custom web apps and enterprise knowledge infrastructure from problem framing to production in weeks.

This is a mid-level seat with real ownership. You will not just consume off-the-shelf AI tools; you will build custom MCP servers reusable Claude Skills RAG pipelines and the semantic and judgement layers that make every agent trustworthy at scale.

Accountabilities

• Ship AI-native internal products. Build and own the agentic workflows copilots and internal tools that investment origination investor relations operations HR finance and the value creation team every day.
• Build the platform layer. Custom MCP servers exposing LLRs data to every agent; RAG pipelines with chunking embeddings vector stores retrieval/generation and evals; reusable Claude Skills that codify LLR patterns.
• Ship custom internal web apps. js React or Streamlit front-ends that put agents in the hands of non-technical users polished fast production-ready.
• Contribute to the knowledge graph. Help stand up the enterprise knowledge graph and semantic search that turn LLRs data into one queryable brain.
• Build the judgement layer. LLM-as-judge evals deterministic assertions guardrails and observability plus approval flows confidence thresholds and escalation paths so no agent output reaches an LP an IC or a portfolio company without a person in the loop.
• Bring rigor. Instrument everything adoption usage hours returned so the value of every agent is measured not hoped for.
• Partner across the firm. Sit with deal teams origination IR operations HR finance and the Value Creation Team to identify their highest-leverage workflows and ship for them end-to-end.
• Drive AI adoption across the firm. Run regular trainings and office hours write playbooks and sit with users until the tool is habitual an agent nobody uses is a cost not an asset.

Skills and Requirements

• Ability to work in-person in LLRs Philadelphia office
• 2-4 years of professional software engineering with at least 1 year shipping production LLM applications agents or retrieval systems to real users.
• Strong Python (async typing testing); TypeScript or Streamlit for shipping custom web apps and internal tools.
• Deep hands-on experience with foundation model APIs and SDKs (Anthropic OpenAI) tool use function calling structured outputs and prompt engineering.
• Built RAG pipelines end-to-end chunking embeddings vector stores (pgvector Pinecone or similar) and retrieval and generation evaluation.
• Built custom MCP servers and reusable Claude Skills not just consumed them. You understand the protocols can design new integrations and know when to reach for a Skill vs. an MCP server.
• AI-native engineer. Daily fluency across Claude and ChatGPT ecosystems Connectors Claude Code Codex Cowork and agentic frameworks (LangGraph PydanticAI DSPy) shipped in production. You know the tradeoffs and pick the right tool per problem.
• Track record as a forward-deployed founding or early engineer on a small high-ownership team youve worked directly with non-technical users on real problems.
• Experience designing for regulated environments data classification PII handling scoped access information barriers and audit logs on every agent action.

Nice to Have

• Experience inside private equity financial services consulting or another regulated document-heavy environment.
• Comfort in an Azure environment including familiarity with Azure AI Foundry with modern deployment platforms (Render Vercel Supabase) and Git-based workflows.
• Experience building harnesses for agents either using a harness framework or standing up your own.
• Knowledge graph or GraphRAG experience bonus for enterprise search architectures at scale.
• Experience communicating technical work to non-technical stakeholders through writing decks and live demos.
• Flexibility with project management and workflow tools (JIRA Trello Linear Asana or similar).
• Working knowledge of PE-stack data (PitchBook SourceScrub Grata Allvue Chronograph) and the deal lifecycle IC memos LP reporting fund structures enough to build useful tools without a translator.
• Curiosity about and informed perspective on the evolving AI and agent ecosystem.
• Awareness of token economics and inference cost model selection prompt caching routing small vs. frontier models by task.
• Experience extracting structure from messy documents PDF parsing table extraction meeting transcripts email threads.
• Observability tooling for agents in production LangSmith Langfuse Braintrust or similar for tracing and debugging.