> Markdown version of [/jobs/ext/4034558-forward-deployed-engineer](https://www.wearedevelopers.com/jobs/ext/4034558-forward-deployed-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Forward Deployed Engineer - **Company:** LLR Partners - **Location:** Philadelphia, PA, United States - **Contract:** Permanent contract - **Skills:** LangGraph Framework, Application Programming Interfaces (APIs), Artificial Intelligence, Automated Storage and Retrieval Systems, Asana, JIRA, Audit Trail, Microsoft Azure, Software Debugging, Graph Database, Design of User Interfaces, Human-Computer Interaction, Python (Programming Language), PostgreSQL, Parsing, OpenAI, Next.js, Search Technologies, Software Engineering, Trello, TypeScript, Web Applications, Workflow Management Systems, Enterprise Search, Datadog, Pinecone, Data Classification, ReactJS, Retrieval-Augmented Generation, DSPy (Programming Framework), Large Language Models, Claude Code, Prompt Engineering, Langfuse — LLM Observability and Analytics Platform, Pgvector, Git Flow, LangSmith, PydanticAI Framework, Claude, Invoking Functions, Graph RAG, Streamlit Framework, GPT - **Published:** October 30, 2026 - **Apply:** https://www.careerbuilder.com/job-details/forward-deployed-engineer-philadelphia-pa--b6230aef-a6ee-4f42-a892-12095b36ca7b ## About the Role Memory Hardware, Internet Application, Artificial Intelligence (AI), MCP - Microsoft Certified Professional, Finance, Investor Relations, Shallow Parsing, User Interface/Experience (UI/UX), React.js, Semantic Search, * 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. ## Description 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 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.