{"id":3461550,"url":"https://alion.io/job/integrant-senior-lead-agentic-developer","title":"Senior Lead Agentic Developer","company":{"id":5272,"name":"Integrant","domain":"integrant.com","url":"https://alion.io/company/integrant","size_band":"201-500","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workable","truth_index":{"grade":"C","score":69,"open_postings":28,"ghost_share":0.5,"stale_share":0,"repost_share":0.036,"time_to_fill_p50_days":45,"computed_at":"2026-10-31T05:58:55Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"lead","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Cairo, Egypt"],"countries":["EG"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":17500,"max_usd":43000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":1286},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Arize Phoenix","optional":false},{"name":"AutoGen","optional":false},{"name":"AWS Bedrock","optional":false},{"name":"AWS Step Functions","optional":false},{"name":"Copilot Studio","optional":false},{"name":"CrewAI","optional":false},{"name":"GraphQL","optional":false},{"name":"HPC","optional":false},{"name":"Human-in-the-Loop","optional":false},{"name":"Hybrid Search","optional":false},{"name":"ITSM","optional":false},{"name":"Langfuse","optional":false},{"name":"LangGraph","optional":false},{"name":"LangSmith","optional":false},{"name":"Least Privilege","optional":false},{"name":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"Machine Learning","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"Multi-Agent Systems","optional":false},{"name":"OpenAI Agents SDK","optional":false},{"name":"OpenTelemetry","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Pydantic","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Rest API","optional":false},{"name":"SLI/SLO/SLA","optional":false},{"name":"TypeScript","optional":false},{"name":"Zod","optional":false},{"name":"AWS","optional":true},{"name":"Azure","optional":true},{"name":"CI/CD","optional":true},{"name":"Fine-tuning","optional":true},{"name":"GCP","optional":true},{"name":"GDPR","optional":true},{"name":"HIPAA","optional":true},{"name":"Knowledge Distillation","optional":true},{"name":"LangChain","optional":true},{"name":"Model Distillation","optional":true},{"name":"SOC 2","optional":true}],"status":"live","first_seen_at":"2026-10-29T14:03:49Z","employer_posted_date":"2026-10-29","last_verified_at":"2026-10-31T05:13:28Z","board_verified":true,"closed_at":null,"days_open":1,"trust":{"level":"ok","repost_count":0,"flags":["company_stale"],"days_open":1},"description":"About Integrant\nFor 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.\nRole overview\nTitle: Agentic Developer (Enterprise Agentic Systems)\nMission: Build production-grade AI agents that run real business processes inside enterprise environments - reliably, securely, observably, and with measurable ROI.\nThis 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.\nStack-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.\nKey responsibilities:\n1. Agent design & orchestration\nDesign single-agent and multi-agent systems: planner/executor, supervisor/worker, hierarchical, and event-driven patterns, choosing the simplest pattern that meets the requirement.\nModel agent workflows as explicit state machines or graphs with defined states, transitions, retries, timeouts, and human-in-the-loop checkpoints.\nDefine tool contracts (schemas, idempotency, error semantics) and implement tool-calling with validation and graceful degradation.\nImplement memory strategies: short-term context management, long-term memory stores, and session persistence with clear retention rules.\n2. Retrieval & knowledge (RAG at scale)\nBuild and tune retrieval pipelines: chunking, embedding, hybrid search, re-ranking, metadata filtering, and query rewriting.\nHandle enterprise document realities: permissions-aware retrieval, multi-tenant isolation, freshness and sync from source systems.\nMeasure retrieval quality (recall, precision, groundedness) and fix it at the pipeline level, not the prompt level.\n\n3. Evaluation, testing & quality\nBuild eval suites before shipping: golden datasets, task-level success metrics, LLM-as-judge with calibration, regression tests on every change.\nWrite unit and integration tests for tools, state transitions, and guardrails; test the non-LLM 80% like any other software.\nRun offline and online evals; use A/B or shadow deployments to validate changes against production traffic.\n\n4. Observability & operations\nInstrument agents end-to-end: traces per run, token/cost accounting, latency per step, tool-call outcomes, and failure classification.\nDefine SLOs for agents (success rate, p95 latency, cost per task) and build dashboards and alerts against them.\nOperate agents in production: on-call participation, incident analysis, root-cause on model and non-model failures.\n\n5. Security, safety & governance\nImplement defenses against prompt injection, data exfiltration, and tool misuse; enforce least-privilege for every tool and integration.\nApply input/output guardrails, PII handling, content policies, and audit logging that satisfies enterprise compliance.\nDesign human-approval gates for high-impact actions and make agent decisions explainable to auditors and business owners.\n\n6. Enterprise integration\nIntegrate agents with ERP, CRM, ITSM, document management, and identity systems via APIs, event streams, and MCP-style tool servers.\nHandle authentication and authorization on behalf of users (OAuth, service principals, delegated access) correctly.\nBuild agents that survive real data: inconsistent records, partial failures, rate limits, and legacy interfaces.\n\n7. Delivery & collaboration\nTranslate business processes into agent specifications with clear success criteria and ROI measures.\nProduce architecture decision records, runbooks, and handover documentation.\nMentor engineers transitioning into agentic development; review designs and code for agent-specific pitfalls.\nRequirements\nMust-have\n8+ years of professional software engineering, with at least 1-2 years shipping LLM-based systems to production users.\nStrong in Python or TypeScript, working proficiency in the other; comfortable with async programming, API design, and typed data models (e.g., Pydantic/Zod).\nHas 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 fixed.\nHands-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 calls.\nPractical RAG experience: vector and hybrid search, chunking strategy, re-ranking, and retrieval evaluation.\nExperience writing evals for LLM systems and treating prompt changes as code changes with regression coverage.\nWorking knowledge of observability tooling (OpenTelemetry-style tracing, LLM tracing platforms such as LangSmith, Langfuse, Arize, or equivalent).\nUnderstanding of LLM security threats (prompt injection, data leakage, excessive agency) and standard mitigations.\nExperience integrating with enterprise systems through REST/GraphQL APIs, webhooks, or message queues, including auth flows (OAuth 2.0, service accounts).\nExperience with workflow engines (Temporal, or Durable Functions, or Step Functions) for long-running agent processes.\nModel Context Protocol (MCP) server or client implementation.\n\nNice-to-have\nCloud deployment experience (Azure, AWS, or GCP) with containers and CI/CD\nFine-tuning or distillation experience, or structured-output and function-calling optimization at scale.\nKnowledge-graph or semantic-layer experience for grounding agents.\nExposure to regulated environments (SOC 2, HIPAA, GDPR) and audit requirements.\nPrior consulting or client-facing delivery experience.\nBenefits\nCompetitive compensation package \nPTO, full medical and dental coverage, etc. \nOpportunity to travel and work onsite with U.S. customers \nIn-house Technical and English training programs \nDedicated learning time (check out our 4Plus1 Program) \nInterest free loans \nFlexible work schedules \nPerks: events, sponsored lunch, game area, rooftop hangout + more!","description_format":"text","description_chars":6697,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":false},"security_clearance":false,"languages":[]},"benefits":["Flexible 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