JOB DESCRIPTION

Head of AI Engineering

& Center of Excellence (CoE)

1. Role Overview

Our company is seeking a senior technology leader to establish and lead its AI Engineering Center of Excellence (CoE), driving the adoption of AI across enterprise engineering, insurance technology platforms, product development, and operational workflows.

The Head of AI Engineering & CoE will define AI engineering strategy, establish reusable AI architectures and frameworks, enable AI-assisted software development, and deliver scalable, secure, measurable AI solutions. The role combines technical leadership, architecture governance, engineering enablement, and people development.

A core objective is to turn individual AI experiments and isolated implementations into reusable enterprise capabilities that support Solartis's product-platform strategy.

1. Key Responsibilities

A. AI Engineering Strategy and CoE Leadership

Establish the CoE charter, operating model, service offerings, engagement model, and delivery priorities.

Develop an AI engineering roadmap aligned with product strategy, technology priorities, and business objectives.

Identify opportunities to standardize AI capabilities and reduce duplicated implementations.

Define reference architectures, reusable patterns, implementation guidelines, and approved technology frameworks.

Partner with the CTO and engineering leadership to prioritize AI investments based on business value, feasibility, risk, and expected return.

Establish processes to evaluate AI initiatives, productionize successful pilots, and retire ineffective or duplicated solutions.

B. Enterprise AI Architecture and Shared Frameworks

Define scalable architectures for LLMs, SLMs, RAG, vector search, orchestration, and model integration.

Design reusable AI services, APIs, SDKs, libraries, reference implementations, and deployment patterns.

Establish patterns for model selection, prompt management, context engineering, embeddings, document processing, inference, and lifecycle management.

Evaluate AI frameworks, models, infrastructure, and platforms for performance, cost, security, scalability, and maintainability.

Define integration patterns for AI capabilities within existing platforms, services, applications, and development workflows.

Guide build-versus-buy decisions across managed AI services, open-source models, and internally developed capabilities.

C. AI-Assisted Software Development Lifecycle (SDLC)

Define and implement AI-assisted SDLC practices across requirements analysis, code generation, code understanding, test generation, code review, documentation, debugging, and maintenance.

Establish standards for responsible use of AI coding assistants and developer productivity tools.

Build reusable workflows and accelerators integrated with development environments, repositories, CI/CD pipelines, and quality processes.

Measure impact on cycle time, defect rates, rework, code quality, and developer experience.

Ensure AI adoption complements peer review, automated testing, security scanning, and release governance.

Work with engineering leaders to drive adoption and develop team-level enablement plans.

D. RAG, MCP, Agentic AI, and Reusable AI Services

Architect production-grade RAG solutions covering ingestion, parsing, chunking, metadata extraction, indexing, retrieval, reranking, and grounded generation.

Establish reusable patterns for agents, tool orchestration, workflow automation, context management, and human-in-the-loop processing.

Define appropriate Model Context Protocol (MCP) patterns for controlled access to enterprise tools, data, and services.

Develop common components for model access, prompt/context management, retrieval, tool integration, evaluation, telemetry, and error handling.

Define safeguards for permissions, data access, execution boundaries, auditability,



failure recovery, and human approval.

Apply agentic AI selectively where it delivers measurable value and avoid unnecessary complexity in deterministic workflows.

E. AI-Assisted Insurance Content Processing and Parser Initiatives

Lead architecture for processing insurance documents, policy forms, underwriting guidelines, rating manuals, and related content.

Guide pipelines that transform semi-structured and unstructured content into validated, structured insurance product definitions.

Establish reusable capabilities for classification, parsing, extraction, semantic interpretation, rule identification, and structured output generation.

Ensure extracted content is traceable to source documents and suitable for downstream product configuration and processing.

Design human review and exception handling for ambiguous, incomplete, conflicting, or low-confidence outputs.

Define evaluation datasets and measures for extraction precision/recall, completeness, rule interpretation, and downstream usability.

Promote reusable parsers and validation components over isolated customer-specific implementations, in collaboration with insurance domain and platform teams.

F. AI Engineering Standards, Quality, and Governance

Define standards for AI design, implementation, testing, deployment, monitoring, and maintenance.

Establish evaluation frameworks for accuracy, groundedness, robustness, reliability, latency, cost, and use-case suitability.

Implement safeguards against prompt injection, unauthorized data exposure, insecure tool execution, and other AI-specific threats.

Define model and prompt versioning, traceability, observability, regression evaluation, and controlled production releases.

Establish practices for sensitive data handling, access control, retention, audit logging, and external AI services.

Partner with security, compliance, legal, and enterprise architecture stakeholders on applicable requirements.

Ensure solutions have appropriate fallbacks, exception handling, human oversight, and production-readiness reviews.

G. Technical Leadership, Mentoring, and Enablement

Provide technical leadership to AI architects and designated AI engineering teams, with accountability for architecture quality and capability development.

Mentor existing architects in AI solution design, reusable architecture, model integration, evaluation, and production engineering.

Establish architecture reviews, technical communities of practice, learning paths, workshops, and hands-on reference projects.

Guide teams in evaluating alternatives, challenging assumptions, conducting impact analysis, and making sound technical decisions.

Develop architecture decision records, implementation templates, reusable documentation, and engineering playbooks.

Build internal capability so teams can implement AI solutions independently within agreed standards.

H. Business Value, Delivery, and Continuous Improvement

Translate opportunities into prioritized initiatives with defined outcomes, milestones, resource needs, and success measures.

Partner with product, engineering, and business teams to improve developer productivity, reduce manual effort, accelerate content processing, and strengthen platform scalability.

Maintain visibility into AI initiative status, adoption, risks, operating costs, and realized perks.

Track production outcomes beyond proof-of-concept delivery and optimize duplicated capabilities, infrastructure,



and model costs.

Communicate progress, risks, decisions, and measurable outcomes to the CTO and senior leadership.

3. Leadership and Decision-Making Expectations

Establish and execute the AI engineering roadmap within priorities agreed with the CTO.

Own the technical direction and engineering standards of the AI CoE.

Lead and develop AI architects and designated AI engineering resources.

Make decisions within agreed enterprise architecture principles and delegated authority; escalate material cross-platform, security, and investment decisions as required.

Challenge designs that introduce unnecessary customization, duplication, operational complexity, or technical risk.

Balance innovation with engineering discipline, production readiness, maintainability, and measurable business outcomes.

Influence teams beyond the direct reporting line and enable adoption across the organization.

4. Required Qualifications and Experience

Bachelor's or master's degree in Computer Science, Engineering, AI, Data Science, or a related discipline.

Typically 15+ years of relevant software engineering, architecture, or technology experience, including substantial practical experience with AI/ML and modern AI application architectures.

Demonstrated experience leading AI engineering initiatives, architecture practices, technical teams, or enterprise AI programs.

Strong practical knowledge of LLM applications, RAG, embeddings, vector databases, prompt engineering, and model evaluation.

Experience designing production-grade AI services, APIs, orchestration workflows, and reusable frameworks.

Practical understanding of agentic AI, tool integration, workflow orchestration, and MCP where relevant.

Experience with Python, APIs, distributed systems, CI/CD, automated testing, and cloud-based application deployment.

Experience evaluating AI models and frameworks for quality, latency, cost, security, and operational complexity.

Knowledge of AI application security, data protection, observability, model lifecycle management, and responsible AI practices.

Demonstrated ability to mentor architects and engineers, influence technical direction, and establish standards across teams.

Strong communication skills, including the ability to explain technical decisions to executives, business stakeholders, and engineering teams.

5. Preferred Qualifications

Experience establishing an enterprise AI Engineering CoE or equivalent AI enablement function.

Experience with comparable enterprise AI platforms and cloud-native AI services.

Experience integrating AI-assisted software development tools into engineering workflows.

Experience in insurance technology, policy administration, rating, product configuration, or document-intensive enterprise platforms.

Experience with intelligent document processing, information extraction, rules extraction, or structured-content generation.

Experience with enterprise knowledge systems, metadata-driven architectures, and reusable platform services.

Familiarity with AI governance frameworks, evaluation methodologies, and production risk management.

6. Leadership Profile

The ideal candidate combines deep technical credibility with the ability to establish a sustainable engineering practice. They can move between architecture design, hands-on problem solving, technical mentoring, organizational enablement, and executive communication.

The candidate should build systems and teams that scale, establish standards without unnecessary bureaucracy, and translate emerging AI technologies into practical business capabilities.

Ultimate expectation: Establish an AI Engineering CoE that strengthens Solartis's product-platform strategy, improves engineering effectiveness, accelerates AI adoption, and builds long-term organizational capability.

📌 Head of AI Engineering & Center of Excellence (CoE) (Chennai)
🏢 Talentgigs
📍 Chennai