Agentic AI Architect

Job Title: Agentic AI Architect Role Summary The Agentic AI Architect is responsible for designing, building, and operationalizing autonomous AI systems (AI agents) that can reason, plan, and execute tasks across enterprise ecosystems. This role combines deep expertise in AI/ML, Generative AI, distributed systems, and enterprise architecture to enable intelligent, self-orchestrating workflows that enhance business productivity and decision‑making. Key Responsibilities 1. Architecture & Design Define end-to-end Agentic AI architecture frameworks leveraging LLMs, multi‑agent systems, and orchestration layers Design autonomous AI agents capable of planning, reasoning, memory management, and tool usage Establish reference architectures for enterprise‑grade AI applications (cloud‑native, scalable, secure) Integrate AI agents with enterprise systems (Data Platforms, ERP, CRM, APIs, and event‑driven systems) 2. Agentic AI Development Build and deploy multi‑agent systems using frameworks such as LangChain, Semantic Kernel, AutoGen, CrewAI, or similar Implement agent orchestration patterns (planner‑executor, reflection loops, self‑healing agents) Develop agents with capabilities including: Task decomposition and planning Contextual reasoning and chaining Memory (short‑term, long‑term, vector‑based retrieval) Tool calling and API integrations 3. GenAI & LLM Integration Architect solutions leveraging leading LLMs (Azure OpenAI, OpenAI, Anthropic, etc.) Implement RAG (Retrieval‑Augmented Generation) pipelines with vector databases (FAISS, Pinecone, Azure AI Search, etc.) Optimize prompt engineering, fine‑tuning, and grounding strategies Ensure efficient token usage, latency, and cost optimization 4. Enterprise Integration Integrate Agentic AI solutions with: Data ecosystems (Azure Databricks, Synapse, Snowflake) Workflow tools (ServiceNow, Power Platform, custom enterprise apps) APIs, microservices, and event‑driven architectures Enable AI‑driven automation across business processes 5. Governance, Security & Responsible AI Define AI governance frameworks (auditability, compliance, explainability) Implement guardrails for safe and responsible AI usage Ensure data privacy, model security, and regulatory compliance Design monitoring mechanisms for hallucination detection and agent reliability 6. Performance Optimization & Scalability Optimize inference performance, caching strategies, and execution flows Design scalable multi‑agent systems across distributed/cloud environments Monitor throughput, reliability, and system health 7. Leadership & Strategy Lead architecture discussions with stakeholders and executive leadership Drive AI adoption strategy and roadmap across business units Mentor engineering teams on Agentic AI best practices Evaluate emerging tools, frameworks, and innovations in AI ecosystems Required Qualifications Education Bachelor’s or Master’s degree in Computer Science, Data Science, AI/ML, or related field Experience 10+ years in software engineering, data engineering, or enterprise architecture 3–5+ years in AI/ML or Generative AI solution development Hands‑on experience with LLM‑based applications and orchestration frameworks Required Technical Skills AI/ML & GenAI LLMs, prompt engineering, fine‑tuning RAG, embeddings, vector databases Multi‑agent architectures and agent frameworks Programming Python (primary), with experience in AI libraries Familiarity with Java/Scala/Node.js (optional but useful) Cloud Platforms Azure (preferred), AWS, or GCP Azure OpenAI, Azure AI Studio, Databricks Data & Platforms Data engineering ecosystems (Databricks, Snowflake, Synapse) REST APIs, microservices, event streaming (Kafka, Event Hub) DevOps & MLOps CI/CD pipelines, Docker, Kubernetes Monitoring, logging, experiment tracking Preferred Skills Experience with Autonomous AI systems / AI agents in production Knowledge of knowledge graphs and semantic search Exposure to reinforcement learning or adaptive systems Experience in Insurance/Financial Services domain (nice to have for enterprise roles like AIG) Key Competencies Strategic thinking and enterprise architecture design Strong problem‑solving and system design skills Excellent stakeholder communication and leadership abilities Ability to translate business requirements into AI solutions Please note this role is not able to offer visa transfer or sponsorship now or in the future We're excited to meet people who share our mission and who can make an impact in a variety of ways. Don't hesitate to apply—even if you only meet the minimum requirements. Think about your transferable experiences and unique skills that make you stand out. Salary and Other Compensation: Applications will be accepted until July 29, 2026 The annual salary for this position is between $90,000 - $150,000 depending on experience and other qualifications of the successful candidate. This position is also eligible for Cognizant's discretionary annual incentive program, based on performance and subject to the terms of Cognizant's applicable plans. Benefits Medical/Dental/Vision/Life Insurance Paid holidays plus Paid Time Off 401(k) plan and contributions Long‑term/Short‑term Disability Paid Parental Leave Employee Stock Purchase Plan Disclaimer: The salary, other compensation, and benefits information is accurate as of the date of this posting. Cognizant reserves the right to modify this information at any time, subject to applicable law. Cognizant is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to sex, gender identity, sexual orientation, race, color, religion, national origin, disability, protected Veteran status, age, or any other characteristic protected by law. #J-18808-Ljbffr Cognizant