Key Responsibilities :

Enterprise AI Strategy & Architecture :

- Define and drive the enterprise AI architecture roadmap.

- Develop AI transformation strategies aligned with business objectives.

- Establish AI reference architectures, standards, governance models, and best practices.

- Drive adoption of AI-first design principles across products and engineering teams.

RAG & Knowledge Systems :

- Design and implement enterprise-grade Retrieval Augmented Generation (RAG) platforms.

- Architect knowledge repositories, vector databases, semantic search systems, and enterprise knowledge assistants.

- Optimize retrieval quality, grounding accuracy, hallucination prevention, and response relevance.

- Build scalable knowledge management ecosystems leveraging structured and unstructured data.

Agentic AI & Multi-Agent Systems :

- Design and implement autonomous AI Agent and Multi-Agent architectures.

- Build intelligent workflows capable of planning, reasoning, orchestration, execution, and decision support.

- Implement Agentic AI frameworks for software development, testing, support, customer operations, underwriting, claims processing, and business workflows.

- Establish agent governance, monitoring, security, and observability practices.

Large Language Models & Small Language Models :

- Evaluate, benchmark, and optimize LLMs and SLMs across business use cases.

- Design hybrid AI architectures combining proprietary, open-source, and commercial models.

- Implement model selection, routing, orchestration, fine-tuning, and optimization strategies.

- Lead model performance, cost optimization, and inference efficiency initiatives.

AI Engineering & Platform Development :

- Build scalable AI platforms supporting enterprise-wide adoption.

- Develop reusable AI services, frameworks, accelerators, SDKs, and APIs.

- Establish AI MLOps, LLMOps, and AgentOps practices.

- Design AI observability, monitoring, evaluation, and governance capabilities.

AI-Powered Engineering Transformation :

- Drive AI-assisted software development initiatives.

- Implement AI-powered code generation, code review, testing, documentation, and DevOps solutions.

- Establish AI-driven SDLC workflows and engineering productivity frameworks.

- Partner with engineering leadership to build AI-enabled development organizations.

Innovation & Emerging Technologies :

- Continuously evaluate emerging AI technologies and industry trends.

- Drive innovation programs, proofs of concept, and technology incubation initiatives.

- Provide thought leadership to executive management and engineering teams.

Required Qualifications :

- Bachelors or Masters degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or related discipline.

- 12+ years of software engineering, architecture, and enterprise platform experience.

- 5+ years of hands-on AI/ML architecture experience.

- Proven experience designing and deploying enterprise AI solutions in production environments.

Mandatory Technical Expertise :

Generative AI :

- OpenAI, Azure OpenAI, Anthropic Claude, Gemini, Llama, Mistral, DeepSeek, Open-source foundation models

RAG Platforms :

- LangChain, LangGraph, LlamaIndex, Vector Databases, Pinecone, Weaviate, Chroma, Azure AI Search

Agentic AI :

- Multi-Agent Systems, AI Agent Frameworks, Agent Orchestration, Agent Planning & Reasoning, Agent Memory Architectures, Agent Governance

AI Platform Engineering :

- LLMOps, MLOps, AgentOps, Prompt Engineering, Model Evaluation, Fine-Tuning, Embeddings, Knowledge Graphs

Cloud & Enterprise Platforms :

- OCI, Azure, AWS, GCP, Kubernetes, Containers, API Platforms, Event-Driven Architectures

Programming :

- Python, .NET, REST APIs, Microservices, Distributed Systems

Preferred Qualifications :

- Experience in Insurance, InsurTech, Financial Services, Healthcare, or Enterprise SaaS platforms.

- Experience building AI copilots, enterprise assistants, autonomous agents, and intelligent workflow platforms.

- Exposure to enterprise governance, security, compliance, and responsible AI frameworks.

- Experience leading AI transformation initiatives across large organizations.

Leadership Competencies :

- Strategic thinking with hands-on execution capability.

- Strong architecture and system design expertise.

- Exceptional communication and stakeholder management skills.

- Ability to influence engineering, product, and business leaders.

- Strong problem-solving and innovation mindset.

- Passion for mentoring and building AI capabilities across teams.