Role Overview

Our company is seeking an accomplished Head of AI Division to lead its enterprise-wide Artificial Intelligence, Generative AI, Agentic AI, and Intelligent Automation initiatives. This is a senior leadership position responsible for defining the AI vision, building and scaling AI capabilities, establishing enterprise AI platforms, and driving measurable business transformation across the organization's engineering, product, and operational ecosystems.

The ideal candidate will bring 20+ years of experience across software engineering, enterprise architecture, technology leadership, and platform development, including substantial hands-on experience in designing and deploying production-grade AI and Generative AI solutions.

The role requires a unique combination of executive leadership, deep technical expertise, product and business acumen, and the ability to transform emerging AI technologies into secure, scalable, and commercially valuable enterprise solutions.

The Head of AI Division will work closely with executive leadership, Engineering, Product, Operations, Architecture, and Customer Success teams to establish AI as a strategic organizational capability, accelerate innovation, improve engineering productivity, and deliver sustainable business value.

Key Responsibilities

1. Enterprise AI Strategy & Business Transformation

- Define and execute Solartis' enterprise AI strategy, vision, and multi-year transformation roadmap.
- Identify opportunities to leverage Generative AI, Agentic AI, intelligent automation, and AI-powered decision systems across products and business functions.
- Translate business objectives into scalable AI initiatives with clearly defined outcomes, investment priorities, and measurable ROI.
- Establish enterprise AI reference architectures, technology standards, implementation frameworks, and reusable accelerators.
- Advise executive leadership on AI adoption, emerging technologies, investment priorities, opportunities, and associated risks.
- Drive an AI-first engineering and product development culture across the organization.

1. AI Division Leadership & Organizational Development

- Build, lead, and scale a high-performing AI division comprising AI architects, AI/ML engineers, Generative AI specialists, platform engineers, and research or innovation teams.
- Define the organizational structure, operating model, hiring strategy, capability development plans, and performance expectations for the AI division.
- Establish clear ownership and accountability across AI strategy, architecture, engineering, platform development, and production operations.
- Mentor senior technical leaders and develop a strong leadership pipeline within the AI organization.
- Foster collaboration across Engineering, Product, Security, Infrastructure, Operations, and business stakeholders.
- Manage resource allocation, delivery priorities, technology investments, and AI program budgets.

1. Enterprise Generative AI & Foundation Model Strategy

- Lead the evaluation, selection, architecture, and enterprise adoption of Large Language Models (LLMs), Small Language Models (SLMs), and open-source foundation models.
- Establish model selection, routing, orchestration, fine-tuning, inference optimization, and deployment strategies aligned with business requirements.
- Drive the adoption of platforms and technologies such as OpenAI, Azure OpenAI, Anthropic Claude, Gemini, Llama, Mistral, and other relevant foundation models.
- Define strategies for balancing model performance, security, latency, scalability, licensing, and inference costs.
- Oversee the development of enterprise AI services, reusable frameworks, APIs, SDKs, and shared platform capabilities.

1. Enterprise RAG & Knowledge Intelligence Platforms

- Lead the architecture and implementation of enterprise-grade Retrieval-Augmented Generation (RAG) platforms and knowledge intelligence systems.
- Establish scalable architectures integrating structured and unstructured enterprise data, semantic search, embeddings, vector databases, and knowledge graphs.
- Drive improvements in retrieval accuracy, contextual relevance, response quality, grounding, and hallucination mitigation.
- Oversee enterprise knowledge assistants, AI copilots, intelligent search solutions, and domain-specific AI applications.
- Establish standards for data access, source attribution, information security, and evaluation of knowledge-grounded AI systems.

1. Agentic AI & Intelligent Automation

- Define the enterprise roadmap for autonomous AI agents,



multi-agent systems, and intelligent workflow orchestration.
- Lead the design and deployment of AI systems capable of planning, reasoning, tool use, task execution, and controlled decision support.
- Drive Agentic AI adoption across software engineering, testing, customer support, business operations, and relevant domain-specific workflows.
- Establish frameworks for agent orchestration, memory management, human-in-the-loop controls, exception handling, and safe execution.
- Define standards for agent observability, evaluation, access control, governance, and operational reliability.
- Ensure that autonomous workflows operate within clearly defined business rules, permissions, and accountability boundaries.

1. AI Platform Engineering & Production Delivery

- Own the architecture, engineering, deployment, and scaling of enterprise AI platforms in production environments.
- Establish engineering practices for MLOps, LLMOps, and AgentOps, including CI/CD, model lifecycle management, testing, monitoring, and continuous improvement.
- Drive the development of production-ready AI applications using robust APIs, microservices, distributed systems, event-driven architectures, and cloud-native platforms.
- Establish end-to-end AI evaluation frameworks covering quality, accuracy, reliability, security, latency, scalability, and cost.
- Ensure AI solutions meet enterprise requirements for availability, resilience, performance, maintainability, and operational support.
- Maintain strong technical oversight of critical architecture decisions, engineering standards, and production implementations.

1. AI-Powered Engineering Transformation

- Lead enterprise-wide adoption of AI-assisted software development and intelligent engineering workflows.
- Drive AI-enabled code generation, code review, automated testing, documentation, developer assistance, and DevOps automation.
- Partner with engineering leadership to identify productivity bottlenecks and establish measurable improvements in software delivery.
- Develop frameworks to assess AI-assisted engineering quality, developer productivity, defect reduction, and delivery efficiency.
- Promote responsible adoption of AI development tools while maintaining software quality, intellectual property protection, and security standards.

1. AI Governance, Security & Responsible AI

- Establish enterprise AI governance frameworks covering responsible AI, privacy, security, risk management, and regulatory compliance.
- Define policies for model access, data protection, prompt injection mitigation, sensitive information handling, and AI application security.
- Ensure appropriate human oversight, auditability, explainability, and traceability for AI-assisted and agent-driven decisions.
- Partner with Security, Legal, Compliance, and Architecture teams to establish risk-based controls for enterprise AI adoption.
- Implement governance mechanisms for model evaluation, production approval, change management, incident response, and continuous monitoring.

1. Technology Innovation & Strategic Partnerships

- Lead AI innovation programs, technology incubation, proofs of concept, and enterprise-scale pilots.
- Evaluate emerging AI frameworks, foundation models, orchestration technologies, and industry developments.
- Establish strategic technology partnerships and guide build-versus-buy decisions.
- Convert promising AI experiments into scalable production capabilities with transparent adoption and business-value criteria.
- Represent the organization in strategic technology discussions and provide thought leadership to executive management.

1. Portfolio, Budget & Business Outcome Management

- Own the AI division's delivery portfolio, investment priorities, resource utilization, and operational performance.
- Establish governance for AI project prioritization, delivery milestones, dependencies, and risk management.
- Define measurable business cases for AI initiatives and track their impact on productivity, operating costs, quality, customer experience, and revenue opportunities.
- Communicate portfolio performance, investment requirements, delivery risks,



and realized business value to executive stakeholders.
- Ensure that AI investments deliver sustainable outcomes rather than isolated proofs of concept.

Required Qualifications
- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or a related discipline.
- 20+ years of overall experience in software engineering, technology architecture, enterprise platforms, and technology leadership.
- Significant experience leading large engineering, architecture, AI/ML, or platform engineering organizations.
- Strong hands-on technical foundation in AI/ML and Generative AI, with demonstrable experience designing and deploying enterprise AI solutions in production.
- Proven track record of defining technology strategy and translating it into successful enterprise-scale implementation.
- Experience building and scaling technical teams, leading senior architects and engineering managers, and managing complex technology portfolios.
- Demonstrated ability to engage with executive stakeholders and align AI investments with business priorities.
- Strong understanding of enterprise architecture, distributed systems, cloud-native platforms, application security, and production engineering.

Mandatory Technical Expertise Candidates must demonstrate substantial expertise across the core areas below. Equivalent technologies are acceptable where the candidate has strong architecture and implementation experience.

Generative AI & Foundation Models

- LLMs, SLMs, prompt engineering, model evaluation, fine-tuning, embeddings, inference optimization, and model orchestration.
- Experience with commercial and open-source models, such as OpenAI, Azure OpenAI, Anthropic Claude, Gemini, Llama, Mistral, or equivalent platforms.

RAG & Knowledge Systems

- Enterprise RAG architecture, semantic search, hybrid retrieval, vector databases, and knowledge-grounded AI applications.
- Experience with frameworks such as LangChain, LangGraph, LlamaIndex, or equivalent.
- Familiarity with vector platforms such as Pinecone, Weaviate, Chroma, Azure AI Search, or equivalent.

Agentic AI & Orchestration

- AI agent architecture, multi-agent workflows, planning, reasoning, tool calling, memory, orchestration, and human-in-the-loop controls.
- Agent governance, observability, evaluation, reliability, and secure execution.

AI Platform Engineering

- Production AI platform development, MLOps, LLMOps, AgentOps, CI/CD, model lifecycle management, monitoring, and evaluation.
- AI application security, governance, scalability, performance, and cost optimization.

Cloud & Enterprise Architecture

- Strong experience with at least one major cloud ecosystem: Microsoft Azure, AWS, or Oracle Cloud Infrastructure (OCI); familiarity with GCP is beneficial.
- Kubernetes, containers, APIs, microservices, distributed systems, and event-driven architectures.
- Experience integrating AI platforms with enterprise applications, data ecosystems, identity systems, and security controls.

Programming & Engineering

- Strong proficiency in Python and practical understanding of AI application development.
- Experience with REST APIs, enterprise integration, and production software engineering.
- Familiarity with .NET and Microsoft enterprise technology ecosystems is advantageous.

Preferred Qualifications
- Experience in Insurance, InsurTech, Financial Services, Healthcare, or enterprise SaaS environments.
- Experience building enterprise AI copilots, intelligent workflow platforms, autonomous agents, and knowledge assistants.
- Experience leading AI transformation across multiple business units or large engineering organizations.
- Exposure to enterprise risk management, regulatory requirements, responsible AI, and security governance.
- Experience managing technology budgets, vendor relationships, strategic partnerships, and executive-level reporting.
- A track record of measurable improvements in business performance through AI adoption.

Leadership Competencies
- Enterprise-level strategic thinking combined with strong technical judgment.
- Proven ability to build, lead, and scale high-performing technology organizations.
- Strong executive communication, stakeholder management, and influencing skills.
- Ability to balance innovation, engineering quality, delivery commitments, security, and business outcomes.
- Excellent problem-solving, decision-making, and organizational change management capabilities.
- A strong mentoring mindset with the ability to develop future AI and engineering leaders.

📌 Head of AI Division – Enterprise AI, Generative AI & Agentic Platforms (Chennai)
🏢 Talentgigs
📍 Chennai