Key Responsibilities

AI Advisory & Solution Shaping

• Partner with business stakeholders to identify, assess, and prioritize AI opportunities that address real business challenges.
• Act as a trusted advisor on AI capabilities, feasibility, implementation approaches, and expected business outcomes.
• Help stakeholders translate ideas and business requirements into practical AI-enabled solutions with clear value propositions, implementation considerations, and success criteria.
• Support AI initiatives from ideation through adoption, ensuring alignment between business objectives and technical implementation.

Enterprise AI Solution Delivery

• Lead end-to-end AI solution delivery from problem definition, solution design, implementation planning, development, integration, deployment, and operation.
• Deliver AI capabilities that can be reused and scaled across multiple business units.
• Translate business needs into practical AI solution options, including architecture, implementation approaches, dependencies, risks, and delivery trade-offs.
• Ensure AI solutions are delivered with measurable business impact and clearly defined success metrics.

AI Solution Architecture & System Design

• Act as the AI engineering specialist for enterprise AI initiatives, advising stakeholders on AI architecture, solution feasibility, implementation strategy, and technology selection.
• Design enterprise AI systems including:

AI services

AI agents

Knowledge retrieval systems

APIs and integrations

Workflow orchestration

Evaluation and monitoring frameworks

Guardrails and fallback mechanisms

• Define reusable AI engineering patterns, reference architectures, and implementation standards to support enterprise adoption.
• Ensure AI solutions meet standards for scalability, security, reliability, maintainability, observability, and operational readiness.

Implementation Planning & Technical Delivery

• Lead technical planning for AI initiatives, including implementation roadmaps, technical dependencies, effort estimation, delivery risks, and release approaches.
• Convert AI concepts into executable implementation plans for engineering teams, vendors, or delivery partners.

Support technical due diligence, solution feasibility assessment, build-versus-buy analysis, and vendor technical evaluation.

• Drive implementation decisions that balance business value, technical complexity, scalability, and operational sustainability.

Hands-on Engineering & Implementation

• Personally contribute to the implementation of critical solution components when required.•Develop prototypes, proof-of-concepts, and production-ready AI solutions.
• Implement or review AI services, APIs, orchestration logic, retrieval pipelines, integrations, automation workflows, and evaluation mechanisms.
• Review technical designs and code to ensure engineering quality, maintainability, security, and alignment to enterprise standards.
• Support troubleshooting and resolution of critical AI production issues.

Applied AI, GenAI & Machine Learning

• Apply modern AI engineering techniques including:

Prompt engineering

AI agent design

Retrieval-Augmented Generation (RAG)

Workflow orchestration

Human-in-the-loop design

AI evaluation and testing

• Design AI systems with appropriate quality controls, monitoring approaches, guardrails, fallback strategies, and performance measurement.
• Support implementation, deployment, and integration of machine learning models where applicable.
• Establish evaluation methods and success criteria to ensure AI solutions deliver reliable and measurable outcomes.

Governance & Responsible AI

• Ensure AI solutions comply with governance, security, privacy, risk, and Responsible AI requirements.
• Embed governance-by-design principles into AI architectures, including auditability, monitoring, access controls, and human oversight.
• Support AI governance processes by providing technical assessments, architecture reviews, implementation considerations, and risk evaluations.

Team & Capability Development

• Mentor and guide AI engineers and technical contributors to strengthen AI engineering capabilities.
• Promote best practices in AI system design, architecture, implementation planning, and engineering excellence.
• Contribute reusable assets, frameworks, technical standards, playbooks, and reference implementations.
• Help establish a strong AI engineering culture focused on production-quality delivery and measurable business outcomes.

Key Interfaces

• Business Units and AI Business Champions
• Technology and Architecture Teams
• Data and Analytics Teams
• Security, Risk, Privacy, and Compliance Teams
• AI Governance Bodies and Steering Committees
• Vendors, Technology Partners, and Delivery Teams

Required Qualifications

Experience

• Significant experience in software engineering, enterprise insurance platforms, intelligent automation, or applied AI solutions.
• Proven experience designing, planning, implementing, and operating complex software or AI solutions in enterprise environments.
• Experience leading AI solution architecture, system design, implementation planning, and production delivery.
• Experience working across business and technology functions to deliver measurable business outcomes.
• Experience in regulated or risk-sensitive industries is an advantage.

Technical Skills

• Strong software engineering fundamentals with experience building enterprise-grade applications, backend services, APIs, integrations, and distributed systems.
• Practical experience delivering applied AI, Generative AI, intelligent automation, or AI-enabled business solutions into production.
• Strong understanding of AI system design patterns including:

LLM applications

Retrieval-Augmented Generation (RAG)

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