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.

• 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)

• Build and deploy multi‑agent systems using frameworks such as Lang Chain, Semantic Kernel, Auto Gen, 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

• 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

• Integrate Agentic AI solutions with:
• Data ecosystems (Azure Databricks, Synapse, Snowflake)
• Workflow tools (Service Now, Power Platform, custom enterprise apps)
• APIs, microservices, and event‑driven architectures
• Enable AI‑driven automation across business processes

• 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

• Optimize inference performance, caching strategies, and execution flows
• Design scalable multi‑agent systems across distributed/cloud environments
• Monitor throughput, reliability, and system health

• 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

• Bachelor’s or Master’s degree in Computer Science, Data Science, AI/ML, or related field

• 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

• LLMs, prompt engineering, fine‑tuning
• RAG, embeddings, vector databases
• Multi‑agent architectures and agent frameworks

• Python (primary), with experience in AI libraries
• Familiarity with Java/Scala/Node.js (optional but useful)

• Azure (preferred), AWS, or GCP
• Azure OpenAI, Azure AI Studio, Databricks

• Data engineering ecosystems (Databricks, Snowflake, Synapse)
• REST APIs, microservices, event streaming (Kafka, Event Hub)

• CI/CD pipelines, Docker, Kubernetes
• Monitoring, logging, experiment tracking

• 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)

• Strategic thinking and enterprise architecture design
• Strong problem‑solving and system design skills
• Excellent stakeholder communication and…