Lead AI Engineer
Lead AI Engineer — Bridge-it AI Team
Location: Remote
Experience: 4–8 years in industry
Team: AI Platform (Copilot)
About Bridge-it
Bridge-it is an early-stage EdTech company building a career-readiness platform for K-12 students, counselors, and districts. Students explore careers, colleges, and non-college pathways; build multi-year plans; and get guidance from an AI Copilot that is grounded in their own plans, school data, and a curated knowledge base. Counselors use the same platform to supervise, nudge, and intervene at scale.
The AI Copilot is the heart of the product — a production LangGraph-based agentic system serving real students, where being right matters more than being fluent. We hold ourselves to strict grounding, safety, and tenant-isolation standards, and we measure everything.
About the role
You will join a small AI team that owns the Copilot end to end: the agentic orchestration graph, retrieval and grounding, safety guardrails, response contracts with the frontend, and the evaluation platform that keeps it honest. We are mid-flight on an ambitious redesign — collapsing multiple experimental graph variants into a single workflow-first architecture with typed claim verification, constitutional rules, and per-turn decision records — and you would step directly into that work.
This is a senior role with a visible leadership runway. Beyond writing excellent code, we expect you to analyze ambiguous product requirements, turn them into well-scoped technical specs and tickets, distribute work across the team, and communicate trade-offs clearly to product and leadership. For the right person, this role grows into a techno-management position.
What you'll do
- Design, build, and operate agentic LLM workflows in LangGraph — routing, planning, parallel retrieval, tool execution, verification, and streaming — serving live student and counselor traffic over WebSockets.
- Own RAG quality : multi-source retrieval across vector stores (Milvus, pgvector), a knowledge graph (Neo4j), and structured platform data; chunking strategy; reranking; and grounded generation with typed claims and evidence citation.
- Enforce safety and trust : input/output guardrails, constitutional rules, faithfulness verification, abstain/escalate behavior, and strict multi-tenant data isolation.
- Build and extend our evaluation platform : online LLM-as-judge evaluators, offline regression suites on curated datasets (LangSmith), latency baselines, and A/B experimentation (PostHog feature flags).
- Drive latency and cost optimization : model selection across providers (OpenAI, Gemini, open-weight models via OpenRouter), prompt and context engineering, selective retrieval, caching, and streaming intermediate responses.
- Instrument and debug production behavior with LangSmith tracing, OpenTelemetry, New Relic, and Sentry ; treat live-QA findings as first-class inputs to design.
- Partner with backend (Django/Postgres), frontend (Next.js), and product teams on response envelopes, interaction contracts, and rollout plans.
-
Lead by doing : break epics into staged implementation plans with acceptance criteria, sequence work across engineers, review code, and mentor teammates.
What we're looking for (required)
-
4–8 years of professional software engineering experience, with at least 2–5 years building LLM-powered products in production (not just prototypes or notebooks) and 2 years of leading a team.
- Strong Python engineering fundamentals — typing, testing, async, service design.
- Strong grasp of agentic architecture patterns — supervisor/executor splits, planner–retriever–verifier pipelines, tool/capability registries, routing and fallback tiers — and sound judgment about when a workflow should be deterministic versus agentic.
- Hands-on experience with agent orchestration frameworks (LangGraph strongly preferred; LangChain, or comparable state-machine/agent frameworks acceptable) including multi-step graphs, tool