{"id":1298700,"url":"https://alion.io/job/genzeon-ai-architect-2","title":"AI Architect","company":{"id":3171701,"name":"Genzeon","domain":"genzeon.com","url":"https://alion.io/company/genzeon","size_band":null,"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":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":31000,"max_usd":74000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":11},"experience_years_min":8,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"AI Agents","optional":false},{"name":"AutoGen","optional":false},{"name":"AWS","optional":false},{"name":"AWS Bedrock","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"CrewAI","optional":false},{"name":"Docker","optional":false},{"name":"Embeddings","optional":false},{"name":"FAISS","optional":false},{"name":"Fine-tuning","optional":false},{"name":"Function Calling","optional":false},{"name":"GCP","optional":false},{"name":"HIPAA","optional":false},{"name":"Hugging Face","optional":false},{"name":"Kubernetes","optional":false},{"name":"LangChain","optional":false},{"name":"LangGraph","optional":false},{"name":"LLM","optional":false},{"name":"LLMOps","optional":false},{"name":"Milvus","optional":false},{"name":"Multi-Agent Systems","optional":false},{"name":"OpenAI","optional":false},{"name":"Pinecone","optional":false},{"name":"Platform Engineering","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"RAG","optional":false},{"name":"Scikit-learn","optional":false},{"name":"Semantic Kernel","optional":false},{"name":"TensorFlow","optional":false},{"name":"Tool Use","optional":false},{"name":"Vertex AI","optional":false},{"name":"Weaviate","optional":false},{"name":"Edge AI","optional":true},{"name":"Machine Learning","optional":true}],"status":"live","first_seen_at":"2026-08-21T00:00:00Z","employer_posted_date":"2026-08-21","last_verified_at":"2026-10-02T21:18:32Z","board_verified":true,"closed_at":null,"days_open":43,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":43},"description":"GENZEON SERVICES AI Architect GenAI, Agentic AI & AI-Native Engineering EXPERIENCE 8 - 12 years LEVEL Senior Architect LOCATION India (Hybrid) - Genzeon offices EMPLOYMENT TYPE Full-Time FUNCTION Technology / AI & Engineering REPORTS TO Head of AI / Engineering Leadership About Genzeon Genzeon Services is a technology and business process solutions company that helps healthcare payers, providers, and life sciences organizations modernize their operations through digital transformation, automation, and applied AI. We combine deep domain expertise with modern engineering practices to build intelligent, scalable systems that solve real business problems.Role Summary We are looking for an experienced AI Architect who has worked extensively on AI-led projects and AI-Native Engineering to design, build, and scale intelligent systems across our client engagements. This role goes beyond bolting AI onto existing applications - the ideal candidate has architected solutions where AI (including LLMs and agentic systems) is a first-class, foundational component of the system design. You will define architecture standards, lead technical delivery, and act as the primary AI technology advisor across multiple engagements and teams.\nKey\nResponsibilities AI-Native Solution & Enterprise Architecture\nArchitect end-to-end AI-native systems - applications designed from the ground up around AI capabilities rather than retrofitted with AI features.\nDefine reference architectures, design patterns, and technical standards for AI/ML and GenAI solutions across the organization.\nEvaluate and select the right architecture (RAG, fine-tuning, agentic workflows, hybrid retrieval, multi-model orchestration) based on business requirements, cost, latency, and accuracy trade-offs.\nOwn technical decision-making on model selection, data architecture, integration patterns, and scalability for AI systems.\nEnsure AI solution designs address security, data privacy, compliance (including healthcare/HIPAA where applicable), and responsible AI principles. GenAI, LLMs & Agentic AI\nDesign and oversee implementation of LLM-powered applications, including RAG pipelines, prompt engineering frameworks, and fine-tuning/adaptation strategies.\nArchitect multi-agent and agentic AI systems (planning, tool use, memory, orchestration) for complex, multi-step business workflows.\nDrive adoption of frameworks such as LangChain, LangGraph, Semantic Kernel, AutoGen, CrewAI, or equivalent for building production-grade agentic solutions.\nStay current with the rapidly evolving GenAI ecosystem (foundation models, vector databases, evaluation frameworks) and translate emerging capabilities into practical solution designs. MLOps & AI Platform Engineering\nDefine and guide implementation of MLOps/LLMOps practices - CI/CD for models and prompts, automated evaluation, monitoring, and observability for AI systems in production.\nArchitect scalable AI infrastructure across cloud platforms (Azure, AWS, or GCP), including vector stores, model-serving layers, and orchestration pipelines.\nEstablish practices for model/prompt versioning, cost monitoring, performance benchmarking, and continuous improvement of deployed AI systems.\nPartner with data engineering to ensure high-quality, well-governed data pipelines feeding AI systems. Leadership & Stakeholder Collaboration\nAct as the primary technical advisor on AI architecture for client engagements, presales, and internal capability building.\nCollaborate with product owners, business stakeholders, and delivery teams to translate business problems into feasible, well-architected AI solutions.\nMentor engineers and data scientists on AI-native design principles, MLOps practices, and responsible AI development.\nContribute to proposals, solution estimations, and technical due diligence for new AI opportunities. Required\nQualifications\n8-12 years of overall technology experience, with at least 4-5 years focused substantially on AI/ML architecture and delivery.\nDemonstrated track record architecting and delivering production AI systems - not just prototypes or POCs.\nHands-on depth in Generative AI and LLMs: RAG architectures, prompt engineering, embeddings, vector databases (e.g., Pinecone, Weaviate, FAISS, Milvus), and fine-tuning approaches.\nPractical experience designing or implementing agentic AI systems (multi-agent orchestration, tool-calling, autonomous workflows).\nStrong foundation in traditional ML/AI (supervised/unsupervised learning, model evaluation) in addition to GenAI.\nProficiency in Python and familiarity with ML/AI frameworks (PyTorch, TensorFlow, Hugging Face, scikit-learn).\nSolid understanding of cloud-native architecture and AI services on Azure, AWS, or GCP (e.g., Azure OpenAI, AWS Bedrock, Vertex AI).\nExperience with MLOps/LLMOps tooling and practices - containerization (Docker/Kubernetes), CI/CD, model monitoring, and observability.\nStrong software architecture fundamentals - APIs, microservices, event-driven design, and system integration patterns.\nExcellent communication skills with the ability to explain complex AI concepts to both technical and non-technical stakeholders. Preferred / Good to Have\nExperience in healthcare, payer/provider, or life sciences domains.\nFamiliarity with agentic frameworks such as LangGraph, AutoGen, CrewAI, or Semantic Kernel.\nExposure to responsible AI practices - model governance, bias evaluation, and explainability.\nPrior experience in a client-facing architect or technical lead role within a consulting or IT services environment.\nRelevant certifications (e.g., Azure AI Engineer, AWS Machine Learning Specialty, Google Cloud ML Engineer).\nEducation\nBachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related field (or equivalent practical experience).\nWhat\nWe Offer The opportunity to architect cutting-edge AI-native systems at enterprise scale, work alongside a collaborative engineering culture, and shape Genzeon's AI capability and delivery standards across high-impact client engagements.","description_format":"text","description_chars":6048,"description_truncated":false,"requirements":{"experience_years_min":8,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":true},"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Health Care"],"lifecycle":[{"event":"open","at":"2026-09-26T10:33:09Z"}],"liveness":{"score":33,"band":"fade","label":"Fading","p_open":1,"p_active":0.606,"p_room":0.55,"age_days":42,"expected_fill_days":30,"reasons":["conf:7","win:tail"],"computed_at":"2026-10-02T05:45:00Z"},"pay":null,"html_url":"https://alion.io/job/genzeon-ai-architect-2","json_url":"https://alion.io/job/genzeon-ai-architect-2.json","meta":{"generated_at":"2026-10-03T01:13:13Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","usage":{"tier":"crawler","counted_by":"address","units_charged":1,"used_today":1105,"day_limit":5000,"remaining_today":3895,"minute_limit":60,"resets_at":"2026-10-04T00:00:00Z"}}}