{"id":3139158,"url":"https://alion.io/job/photon-agenticai-workflow-engineer-onsite","title":"AgenticAI Workflow Engineer | Onsite","company":{"id":3186451,"name":"Photon","domain":"photon.com","url":"https://alion.io/company/photon-com","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Oracle","truth_index":{"grade":"D","score":51,"open_postings":701,"ghost_share":0.812,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-10-26T05:57:25Z"}},"role":"Backend","role_family":"Backend","seniority":"middle","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Dallas, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":61000,"max_usd":120000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":1539},"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"CrewAI","optional":false},{"name":"Docker","optional":false},{"name":"ElasticSearch","optional":false},{"name":"Embeddings","optional":false},{"name":"FastAPI","optional":false},{"name":"Function Calling","optional":false},{"name":"GCP","optional":false},{"name":"Hallucination","optional":false},{"name":"Human-in-the-Loop","optional":false},{"name":"LangChain","optional":false},{"name":"LangGraph","optional":false},{"name":"LlamaIndex","optional":false},{"name":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"Multi-Agent Systems","optional":false},{"name":"pgvector","optional":false},{"name":"PostgreSQL","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Redis","optional":false},{"name":"Rest API","optional":false},{"name":"Semantic Kernel","optional":false},{"name":"Semantic Search","optional":false},{"name":"AutoGen","optional":true},{"name":"Claude","optional":true},{"name":"Gemini","optional":true},{"name":"Llama","optional":true},{"name":"Mistral","optional":true},{"name":"OpenAI","optional":true}],"status":"live","first_seen_at":"2026-08-04T21:57:22Z","employer_posted_date":"2026-08-04","last_verified_at":"2026-10-26T10:07:28Z","board_verified":true,"closed_at":null,"days_open":83,"trust":{"level":"ok","repost_count":0,"flags":["company_stale"],"days_open":82},"description":"Agentic AI Workflow Engineer\nWe are seeking an Agentic AI Workflow Engineer to design, build, and optimize intelligent AI-driven workflows using Large Language Models (LLMs), AI agents, and enterprise automation frameworks. You will develop agentic applications that can reason, retrieve knowledge, interact with enterprise systems, and automate complex business processes.\nThe ideal candidate combines strong software engineering fundamentals with hands-on experience in Generative AI application development, agent orchestration, RAG pipelines, prompt engineering, and API integrations.\nTechnical Stack:\nLLMs:\nOpenAI GPT, Claude, Gemini, Llama, Mistral, and other open-source LLMs.\nAgent Frameworks:\nLangGraph, LangChain, LlamaIndex, Semantic Kernel, CrewAI, AutoGen.\nAgentic AI Concepts:\nMulti-Agent Systems (MAS), Agent Planning, Tool Calling, Memory Management, Human-in-the-Loop (HITL) workflows.\nDevelopment:\nPython, FastAPI, REST APIs, Async Programming.\nRAG & Knowledge Engineering:\nVector Databases, PostgreSQL, pgvector, Redis Vector Search, Elasticsearch, Embeddings, Semantic Search, Retrieval Optimization.\nWorkflow Orchestration:\nLangGraph workflows, Agent State Management, Workflow Automation, Event-driven workflows.\nCloud & Deployment:\nAWS/Azure/GCP, Docker, CI/CD pipelines, API deployment.\nTools:\nPrompt Engineering, AI Workflow Design, LLM Evaluation, Agent Monitoring, GenAI Optimization.\nKey Responsibilities:\nDevelop and orchestrate AI agent workflows using LangGraph, LangChain, and multi-agent architectures.\n\nDesign agent behaviors including:\n\nGoals and instructions\n\nTool usage\n\nReasoning flows\n\nMemory management\n\nError handling and recovery\n\nBuild RAG-based AI applications by integrating enterprise knowledge sources, vector databases, and embedding models.\n\nDevelop AI agents capable of interacting with enterprise systems through APIs, databases, and external tools.\n\nImplement function calling and tool integrations enabling agents to perform real-world actions.\n\nCreate reusable agent components, workflow templates, and AI automation patterns.\n\nDevelop backend services and APIs using Python, FastAPI, and asynchronous programming.\n\nOptimize prompts, agent workflows, and retrieval strategies to improve:\n\nAccuracy\n\nResponse quality\n\nLatency\n\nCost efficiency\n\nImplement Human-in-the-Loop workflows for approval-based enterprise processes.\n\nBuild evaluation pipelines to measure agent performance, hallucination rates, and task completion accuracy.\n\nDeploy and monitor GenAI applications using cloud platforms, containerization, and observability tools.\n\nCollaborate with AI architects, product managers, and domain teams to convert business processes into agentic AI solutions.\n\nRequired Qualifications:\n3-6 years of experience in software engineering, AI engineering, or Generative AI application development.\n\nHands-on experience building LLM-powered applications using Python.\n\nStrong understanding of:\n\nLLM concepts\n\nPrompt engineering\n\nRAG architecture\n\nAI agent workflows\n\nVector search concepts\n\nExperience with agent frameworks such as:\n\nLangGraph\n\nLangChain\n\nLlamaIndex\n\nSemantic Kernel\n\nCrewAI\n\nExperience integrating LLM applications with REST APIs, databases, and enterprise systems.\n\nKnowledge of vector databases, embeddings, semantic search, and retrieval optimization techniques.\n\nExperience developing production-quality Python applications using FastAPI or similar frameworks.\n\nFamiliarity with Docker, cloud deployment, CI/CD practices, and API security.\n\nUnderstanding of AI evaluation techniques including:\n\nResponse quality assessment\n\nPrompt testing\n\nAgent workflow validation\n\nExposure to AI governance concepts:\n\nResponsible AI\n\nGuardrails\n\nData privacy\n\nPrompt injection prevention\n\nPreferred Qualifications:\nExperience building autonomous AI agents or multi-agent workflows.\n\nExperience with enterprise automation, IT operations, customer service, or business process automation use cases.\n\nExperience with observability platforms for monitoring AI applications.\n\nContributions to open-source AI frameworks or GenAI 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