OverviewYou will work shoulder-to-shoulder with LLR's 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 LLR's 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 LLR's 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 LLR's 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, Next.js 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 - you've 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.
