{"id":2394209,"url":"https://alion.io/job/exavalu-ai-technical-architect","title":"AI Technical Architect","company":{"id":3802017,"name":"Exavalu","domain":"exavalu.com","url":"https://alion.io/company/exavalu","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Zoho Recruit","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"staff","employment_type":"full_time","work_mode":"remote","remote_scope":"unspecified","remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"inferred","locations":[],"countries":[],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":152000,"max_usd":309000,"period":"year","method":"global_role_seniority_cell","sample_n":1099},"experience_years_min":12,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"CrewAI","optional":false},{"name":"Docker","optional":false},{"name":"Embeddings","optional":false},{"name":"Function Calling","optional":false},{"name":"GCP","optional":false},{"name":"Kubernetes","optional":false},{"name":"LangChain","optional":false},{"name":"LangGraph","optional":false},{"name":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"LLMOps","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"AutoGen","optional":true},{"name":"Hugging Face","optional":true},{"name":"Knowledge Graph","optional":true},{"name":"Langfuse","optional":true},{"name":"LangSmith","optional":true},{"name":"MLFlow","optional":true},{"name":"Model Context Protocol","optional":true},{"name":"OpenTelemetry","optional":true},{"name":"Semantic Kernel","optional":true}],"status":"live","first_seen_at":"2026-10-12T22:10:45Z","employer_posted_date":"2026-10-12","last_verified_at":"2026-10-13T11:56:31Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"This is a remote position.\nAI Technical Architect - Multi-Agentic AI\nExperience: 12+ Years | Type: Full-Time\nJob Summary\nWe are seeking a hands-on AI Technical Architect with strong experience in Generative AI and Multi-Agentic AI to design and implement enterprise AI solutions from POC through production. The role requires expertise in agent orchestration, RAG, LLMs, enterprise integrations, cloud, and AI governance.\nKey Responsibilities\nDesign Multi-Agentic AI architectures using agents, supervisors, planners, routers, and orchestration frameworks.\n\nBuild AI solutions using Python, LangChain, LangGraph, CrewAI, and LLM APIs.\n\nDesign RAG solutions, including embeddings, vector search, retrieval, and prompt optimization.\n\nIntegrate AI agents with enterprise applications, APIs, databases, and tools.\n\nDesign scalable cloud-native AI solutions using microservices, Docker, and Kubernetes.\n\nEstablish AI security, governance, observability, evaluation, and LLMOps/MLOps practices.\n\nLead POCs, architecture reviews, technical decisions, and mentor AI engineering teams.\n\nRequirements\nRequired Skills\n12+ years of overall technology experience.\n\nStrong hands-on experience with Generative AI and Multi-Agentic AI.\n\nAdvanced Python programming.\n\nStrong hands-on experience with LangChain and LangGraph.\n\nExperience with CrewAI or similar agent frameworks.\n\nStrong knowledge of LLMs, RAG, embeddings, vector databases, prompt engineering, and tool/function calling.\n\nExperience with APIs, microservices, and enterprise integrations.\n\nExperience with Azure, AWS, or GCP.\n\nStrong understanding of AI security, Responsible AI, governance, and guardrails.\n\nPreferred Skills\nMCP and agent interoperability.\n\nHugging Face / Open-source LLMs.\n\nLangSmith, Langfuse, MLflow, or OpenTelemetry.\n\nDocker/Kubernetes and LLMOps/MLOps.\n\nSemantic Kernel, AutoGen, or similar frameworks.\n\nKnowledge graphs/graph databases.\n\nExperience building enterprise AI platforms or AI Centers of Excellence.\n\nCore Skills: Multi-Agentic AI | Generative AI | LangChain | LangGraph | CrewAI | Python | RAG | LLMs | MCP | Vector Databases | Cloud\nBenefits\nExavalu also promotes flexibility, adapting to the needs of employees, customers, and the business. It might be part-time work, working outside normal 9-5 business hours or working remotely. We also offer a welcome back program to help individuals return to the mainstream after a prolonged absence due to health or family reasons.\nAt Exavalu, we are committed to building a diverse and inclusive workforce. We welcome applications for employment from all qualified candidates, regardless of race, colour, gender, national or ethnic origin, age, disability, religion, sexual orientation, gender identity or any other status protected by applicable law. 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