{"id":3679922,"url":"https://alion.io/job/iqvia-ai-automation-architect","title":"AI Automation Architect","company":{"id":4918,"name":"IQVIA","domain":"iqvia.com","url":"https://alion.io/company/iqvia","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"B","score":79,"open_postings":547,"ghost_share":0.009,"stale_share":0.817,"repost_share":0.02,"time_to_fill_p50_days":23,"computed_at":"2026-11-04T06:34:25Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"staff","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"explicit","locations":["Warsaw, Poland"],"countries":["PL"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":227000,"max":421400,"currency":"PLN","period":"year","gross":null,"usd_annual":106881},"salary_estimate":null,"experience_years_min":8,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Anthropic","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Claude","optional":false},{"name":"Context Engineering","optional":false},{"name":"Copilot","optional":false},{"name":"Cursor","optional":false},{"name":"Databricks","optional":false},{"name":"Docker","optional":false},{"name":"Embeddings","optional":false},{"name":"FastAPI","optional":false},{"name":"Function Calling","optional":false},{"name":"Hallucination","optional":false},{"name":"Human-in-the-Loop","optional":false},{"name":"ITSM","optional":false},{"name":"Kubernetes","optional":false},{"name":"LangChain","optional":false},{"name":"LangGraph","optional":false},{"name":"LLM","optional":false},{"name":"Microsoft Fabric","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"Multi-Agent Systems","optional":false},{"name":"OpenAI","optional":false},{"name":"Pandas","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"SharePoint","optional":false},{"name":"Snowflake","optional":false},{"name":"Fine-tuning","optional":true},{"name":"LLMOps","optional":true},{"name":"Reinforcement Learning","optional":true}],"status":"live","first_seen_at":"2026-11-03T12:45:30Z","employer_posted_date":"2026-11-03","last_verified_at":"2026-11-04T06:22:15Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"AI Architect (Agentic Process Automation):\nExperience: 8+ Years (minimum 3 years focused on AI/ML, Generative AI, Agentic AI, Copilot, or Intelligent Automation solutions)\nLocation: India / Europe\nEmployment Type: Full-Time\nAbout the Role:\nWe are seeking a hands-on AI Architect to design, govern, and scale enterprise-grade Agentic AI, Copilot, and Intelligent Automation solutions. The role combines AI architecture, enterprise data strategy, evaluation engineering, governance, and intelligent process automation to deliver secure, scalable, and business-driven AI solutions.\nKey Responsibilities:\nArchitecture & Solution Design\nDesign and deliver enterprise-grade Agentic AI, Copilot, RAG, and Intelligent Automation solutions.\nArchitect multi-agent systems including planning, reasoning, memory management, workflow orchestration, tool integration, and Human-in-the-Loop controls.\nDefine enterprise AI strategy, reference architectures, standards, governance frameworks, best practices, and reusable accelerators.\nLead AI platform decisions spanning foundation models, agent frameworks, enterprise data, orchestration platforms, and integration patterns.\nArchitect and provide technical leadership for Python-based AI platforms, agent frameworks, automation services, APIs, and enterprise integrations.\nEnsure AI solutions meet enterprise requirements for scalability, reliability, observability, security, compliance, and operational excellence.\nCollaborate with product, engineering, security, data, and business stakeholders to identify high-value AI opportunities and deliver measurable business impact.\nConduct architecture reviews, support Architecture Review Boards (ARB), and oversee delivery across multiple AI initiatives.\nEnterprise Integration, Data & AI Quality Engineering:\nIntegrate AI solutions with enterprise platforms including ERP, CRM, ITSM, HR, Finance, SharePoint, and other business systems.\nDesign AI architectures leveraging Microsoft Fabric, Databricks, Snowflake, Azure Data Lake, Enterprise Data Platforms, and enterprise knowledge repositories.\nArchitect secure integrations using APIs, Graph APIs, OAuth2, Function Calling, MCP, Agent-to-Agent communication, and event-driven architectures.\nDesign enterprise knowledge architectures across structured, semi-structured, and unstructured data sources.\nDefine data strategies covering knowledge sourcing, metadata management, chunking strategies, embedding architectures, vectorization, and retrieval optimization.\nDesign scalable data processing pipelines, orchestration services, and AI workloads using Python and cloud-native technologies.\nDesign and govern evaluation datasets, benchmark suites, golden test sets, and domain-specific validation frameworks.\nEstablish methodologies for measuring accuracy, groundedness, relevance, reasoning quality, latency, cost efficiency, and business outcomes.\nDefine evaluation pipelines for prompts, RAG systems, copilots, and autonomous agents using automated and human evaluation approaches.\nPartner with business, domain, and data teams to establish trusted AI-ready data foundations, feedback loops, and continuous improvement processes.\nGovernance, Security & Reliability:\nImplement Responsible AI practices covering privacy, security, compliance, auditability, risk management, ethical AI, and PII protection.\nDefine observability, testing, hallucination detection, resiliency, disaster recovery, model monitoring, and operational support strategies.\nEstablish AI quality gates, release criteria, evaluation standards, and governance processes for production AI systems.\nDrive token governance, model routing, semantic caching, cost optimization, and mitigation strategies for bias, drift, and reliability risks.\nFoundation Models & GenAI:\nProvide technical leadership on LLM platforms including OpenAI GPT, Azure OpenAI, Anthropic Claude, Microsoft Copilot, and emerging foundation models.\nApply advanced Prompt Engineering, Context Engineering, Knowledge Grounding, Function Calling, Agent Memory, and Model Routing techniques.\nDesign and optimize Retrieval-Augmented Generation (RAG) solutions leveraging enterprise search, vector databases, embeddings, and knowledge management platforms.\nRequired Qualifications:\nBachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related discipline.\n8+ years of software engineering experience, including a minimum of 3 years focused on AI/ML, Generative AI, Agentic AI, Copilot, or Intelligent Automation solutions.\nStrong expertise in Python, including development of AI agents, APIs, automation services, data processing pipelines, orchestration frameworks, and evaluation platforms.\nHands-on experience building enterprise AI applications using technologies such as FastAPI, LangChain, LangGraph, , Pandas, or similar frameworks.\nHands-on experience with AI development tools such as Cursor AI, GitHub Copilot, and Microsoft 365 Copilot.\nExtensive experience with LLM platforms including OpenAI, Azure OpenAI, Anthropic Claude, and similar technologies.\nDeep understanding of RAG, vector databases, embedding models, enterprise search, knowledge management architectures, and AI evaluation frameworks.\nStrong knowledge of Graph APIs, OAuth2, SSO, enterprise integration patterns, API design, microservices, Docker, Kubernetes, event-driven architecture, and CI/CD pipelines.\nExperience designing AI evaluation datasets, benchmark frameworks, quality gates, model performance metrics, and validation methodologies.\nExperience building AI business cases, value realization frameworks, ROI assessments, and executive-level presentations.\nPreferred Qualifications:\nExperience leading enterprise-scale Agentic AI and multi-agent deployments.\nKnowledge of model fine-tuning, reinforcement learning, and model optimization techniques.\nFamiliarity with Model Context Protocol , AI orchestration platforms, and agent communication protocols.\nExposure to MLOps, LLMOps, prompt management, model monitoring, and AI compliance frameworks.\nIQVIA is a leading global provider of clinical research services, commercial insights and healthcare intelligence to the life sciences and healthcare industries. We create intelligent connections to accelerate the development and commercialization of innovative medical treatments to help improve patient outcomes and population health worldwide. Learn more at https://jobs.iqvia.com\nIQVIA is committed to integrity in our hiring process and maintains a zero tolerance policy for candidate fraud. All information and credentials submitted in your application must be truthful and complete. Any false statements, misrepresentations, or material omissions during the recruitment process will result in immediate disqualification of your application, or termination of employment if discovered later, in accordance with applicable law. We appreciate your honesty and professionalism.\nThe potential base pay range for this role, when annualized, is 227 000,00 zł - 421 400,00 zł. The actual base pay offered may vary based on a number of factors including job-related qualifications such as knowledge, skills, education, and experience; location; and/or schedule (full or part-time). 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