Senior Lead Agentic Developer

About IntegrantFor 34 years, Integrant has designed and delivered advanced, business solutions for U.S. enterprises in biotech, medical devices, manufacturing, finance, and related highly regulated industries. Our teams are experts in traditional web and mobile applications, AI/HPC infrastructure support, data engineering, predictive AI, machine learning, and Agentic solutions. We build long-term partnerships—two-thirds of our customers choosing to partner with us 5+ years—by solving mission-critical problems with rigor, empathy, and measurable outcomes.Role overviewTitle: Agentic Developer (Enterprise Agentic Systems)Mission: Build production-grade AI agents that run real business processes inside enterprise environments — reliably, securely, observably, and with measurable ROI.This is not a prompt-engineering or demo-building role. The person we hire owns the full lifecycle of an agent: design, implementation, evaluation, deployment, monitoring, and iteration. They treat an agent as a distributed software system with a probabilistic component, not as a chatbot with tools attached.Stack-agnostic by design. We use Python and TypeScript, and frameworks such as LangGraph, the OpenAI Agents SDK, Bedrock Agents, and Copilot Studio appear in our work as examples, not requirements. We hire for the underlying engineering judgment: someone who can pick up any orchestration framework in a week because they understand what every framework is abstracting.Key responsibilities: Agent design & orchestrationDesign single-agent and multi-agent systems: planner/executor, supervisor/worker, hierarchical, and event-driven patterns, choosing the simplest pattern that meets the requirementModel agent workflows as explicit state machines or graphs with defined states, transitions, retries, timeouts, and human-in-the-loop checkpointsDefine tool contracts (schemas, idempotency, error semantics) and implement tool-calling with validation and graceful degradationImplement memory strategies: short-term context management, long-term memory stores, and session persistence with clear retention rules Retrieval & knowledge (RAG at scale)Build and tune retrieval pipelines: chunking, embedding, hybrid search, re-ranking, metadata filtering, and query rewritingHandle enterprise document realities: permissions-aware retrieval, multi-tenant isolation, freshness and sync from source systemsMeasure retrieval quality (recall, precision, groundedness) and fix it at the pipeline level, not the prompt level Evaluation, testing & qualityBuild eval suites before shipping: golden datasets, task-level success metrics, LLM-as-judge with calibration, regression tests on every changeWrite unit and integration tests for tools, state transitions, and guardrails; test the non-LLM 80% like any other softwareRun offline and online evals; use A/B or shadow deployments to validate changes against production traffic Observability & operationsInstrument agents end-to-end: traces per run, token/cost accounting, latency per step, tool-call outcomes, and failure classificationDefine SLOs for agents (success rate, p95 latency, cost per task) and build dashboards and alerts against themOperate agents in production: on-call participation, incident analysis, root-cause on model and non-model failures Security, safety & governanceImplement defenses against prompt injection, data exfiltration, and tool misuse; enforce least-privilege for every tool and integrationApply input/output guardrails, PII handling, content policies, and audit logging that satisfies enterprise complianceDesign human-approval gates for high-impact actions and make agent decisions explainable to auditors and business owners Enterprise integrationIntegrate agents with ERP, CRM, ITSM, document management, and identity systems via APIs, event streams, and MCP-style tool serversHandle authentication and authorization on behalf of users (OAuth, service principals, delegated access) correctlyBuild agents that survive real data: inconsistent records, partial failures, rate limits, and legacy interfaces Delivery & collaborationTranslate business processes into agent specifications with clear success criteria and ROI measuresProduce architecture decision records, runbooks, and handover documentationMentor engineers transitioning into agentic development; review designs and code for agent-specific pitfallsRequirementsMust-have8+ years of professional software engineering, with at least 1-2 years shipping LLM-based systems to production usersStrong in Python or TypeScript, working proficiency in the other; comfortable with async programming, API design, and typed data models (e.g., Pydantic/Zod)Has built at least one agent that runs a real multi-step workflow with tool calls in production, and can describe its failure modes and how they were fixedHands-on with at least one orchestration framework (LangGraph, OpenAI Agents SDK, Bedrock Agents, AutoGen, CrewAI, or similar) and able to explain what it abstracts and when to drop to raw API callsPractical RAG experience: vector and hybrid search, chunking strategy, re-ranking, and retrieval evaluationExperience writing evals for LLM systems and treating prompt changes as code changes with regression coverageWorking knowledge of observability tooling (OpenTelemetry-style tracing, LLM tracing platforms such as LangSmith, Langfuse, Arize, or equivalent)Understanding of LLM security threats (prompt injection, data leakage, excessive agency) and standard mitigationsExperience integrating with enterprise systems through REST/GraphQL APIs, webhooks, or message queues, including auth flows (OAuth 2.0, service accounts)Experience with workflow engines (Temporal, or Durable Functions, or Step Functions) for long-running agent processesModel Context Protocol (MCP) server or client implementationNice-to-haveCloud deployment experience (Azure, AWS, or GCP) with containers and CI/CDFine-tuning or distillation experience, or structured-output and function-calling optimization at scaleKnowledge-graph or semantic-layer experience for grounding agentsExposure to regulated environments (SOC 2, HIPAA, GDPR) and audit requirementsPrior consulting or client-facing delivery experienceBenefitsCompetitive compensation package PTO, full medical and dental coverage, etc. Opportunity to travel and work onsite with U.S. customers In-house Technical and English training programs Dedicated learning time (check out our 4Plus1 Program) Interest free loans Flexible work schedules Perks: events, sponsored lunch, game area, rooftop hangout + more!