Principal AI Solutions Architect

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.