New opportunity

Artificial Intelligence Engineer – Artificial Inteligence Solutions & Automation

WTW · Mumbai, India

About this role

The Senior Artificial Intelligence Engineer will be responsible for identifying, developing, implementing, and scaling AI solutions within the CIMS (Captive and Insurance Management Solutions) organization. This hands-on role involves collaborating with business stakeholders to translate business requirements into production-ready AI capabilities, moving initiatives from experimentation to measurable business value. Responsibilities include assessing existing AI pilots, designing and implementing solutions using pro-code and low-code technologies, and ensuring scalability, security, and data privacy. The engineer will create reusable solution patterns and measure impact through metrics like efficiency and quality. Qualifications include a Bachelor’s or Master’s degree in computer science, engineering, or a related field, and typically 5+ years of experience in AI engineering, software engineering, or automation. Candidates must have hands-on experience taking AI solutions from concept to production, with strong proficiency in Generative AI, LLMs, RAG, AI agents, and Python. Preferred qualifications include experience in insurance or financial services, familiarity with Microsoft AI and automation tools (Azure AI, Copilot Studio, Power Platform), and experience scaling AI solutions across global operations.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

Generative AILarge Language Models (LLMs)RAGAI AgentsAutomationPythonAPIsPrompt EngineeringAzure AICopilot StudioPower Platform

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Scaling AI from Pilot to Production
The core mandate is to transition AI initiatives beyond experimentation into production-ready capabilities. You must demonstrate the ability to take existing pilots, assess their viability, and implement them at scale while ensuring they deliver measurable business value.
Bridging Business Needs with Technical Solutions
You will act as a translator between business stakeholders and technical execution. This requires deep understanding of CIMS business processes to identify pain points and design AI solutions that are not only technically sound but also usable and aligned with operational goals.
Developing Reusable AI Patterns
To support global operations, you must create reusable solution patterns. This involves building modular, scalable architectures that can be applied across different CIMS processes, ensuring consistency and efficiency in AI deployment.
Ensuring Governance and Compliance
Operating within a regulated insurance environment requires strict adherence to security, data privacy, and responsible AI standards. You must balance innovation with enterprise-grade governance to ensure all deployed solutions are compliant.

Likely challenges to think through

Navigating Ambiguity in Business Requirements
Business stakeholders may present vague pain points rather than clear technical requirements. Prepare by practicing how to conduct discovery sessions to extract specific, actionable problem statements that can be mapped to AI capabilities like RAG or agentic workflows.
Integrating AI into Legacy Workflows
Integrating modern AI with existing enterprise applications and data repositories is often complex. Prepare by discussing your experience with API-led integration and how you handle data quality or legacy system constraints when deploying new AI agents.
Balancing Pro-code and Low-code Approaches
The role requires flexibility between custom Python development and low-code platforms like Power Platform. Be prepared to justify your architectural choices, specifically when to build custom solutions versus leveraging enterprise-approved low-code tools for speed and maintainability.

Practice your answers

Technical practice

Designing RAG Architectures for Enterprise Data

What to coverExplain how you would design a Retrieval-Augmented Generation (RAG) pipeline to ensure data privacy and accuracy when querying sensitive insurance documents. Focus on the trade-offs between vector database selection, chunking strategies, and retrieval precision.

Scaling AI Solutions from Pilot to Production

What to coverDescribe the technical challenges involved in moving an AI pilot into a production environment. Address how you handle model monitoring, latency, and infrastructure scalability when deploying across global operations.

Integrating LLMs with Enterprise Workflows

What to coverHow do you approach integrating LLMs or AI agents with existing legacy business applications and APIs? Discuss the importance of security, authentication, and ensuring the AI output is reliable for automated decision-making.

Balancing Pro-code and Low-code AI Development

What to coverWhen deciding between a custom Python-based AI solution and a low-code approach using tools like Power Platform or Copilot Studio, what criteria do you use? Suggestion: Focus on maintainability, complexity of the business logic, and speed to market.

Ensuring Responsible AI and Data Privacy

What to coverWhat technical guardrails do you implement to ensure AI solutions comply with data privacy regulations and responsible AI standards? Discuss techniques like PII masking, output validation, and bias mitigation.

Measuring AI Impact and Efficiency

What to coverBeyond model accuracy, what metrics do you track to demonstrate the business value of an AI implementation? Explain how you quantify improvements in turnaround time, operational efficiency, and capacity released.

Prompt Engineering for Complex Business Logic

What to coverHow do you refine prompts to handle ambiguous business requirements while maintaining consistency in model responses? Suggestion: Discuss iterative testing, few-shot prompting, and the use of system instructions to enforce constraints.

Managing AI Agents in Automated Processes

What to coverDescribe your approach to designing AI agents that perform multi-step tasks. How do you handle error handling, state management, and human-in-the-loop requirements within an automated workflow?

Behavioural practice

Translating Ambiguous Business Needs into Technical Requirements

What to coverDescribe a time when you had to work with non-technical stakeholders to define an AI project. How did you clarify their requirements and ensure the final technical solution met their business goals?

Managing Stakeholder Expectations During AI Projects

What to coverTell me about a situation where an AI project faced technical limitations or unexpected challenges. How did you communicate these issues to stakeholders and adjust the project scope or approach?

Collaborating Across Functional Teams

What to coverGive an example of how you have collaborated with IT, governance, or subject matter experts to implement an AI solution. How did you ensure alignment on security and compliance requirements?

Driving Adoption of New AI Capabilities

What to coverDescribe a time when you introduced a new AI tool or process to a team. What steps did you take to ensure the team adopted the solution and realized the intended efficiency gains?

Sharpen your knowledge

Topics to prepare

Production-Ready AI Lifecycle
Review the transition from experimental AI pilots to scalable production environments. Exercise: Map out a hypothetical workflow for moving a RAG-based chatbot from a local notebook to a secure, enterprise-grade deployment, focusing on monitoring, data privacy, and performance metrics.
Responsible AI and Governance
Understand the requirements for deploying AI in regulated industries like insurance. Exercise: Research common AI governance frameworks and draft a checklist for ensuring data privacy and security when integrating LLMs with sensitive corporate document repositories.

Skills to revise

RAG and LLM Integration
Develop proficiency in connecting LLMs to external data sources. Exercise: Build a small Python application using a vector database to perform retrieval-augmented generation on a set of sample insurance policy documents to demonstrate accuracy and source grounding.
Microsoft AI Ecosystem (Azure/Power Platform)
Gain familiarity with enterprise-approved low-code and pro-code tools. Exercise: Create a simple workflow in Power Platform that triggers an Azure AI service to process and classify incoming text data, simulating an automated business process.
AI Agent Orchestration
Learn to design agents that perform multi-step tasks. Exercise: Design a conceptual architecture for an AI agent capable of automating a specific business process, such as extracting data from forms and updating a CRM, while handling error states and human-in-the-loop requirements.

Your preparation checklist

Prepare a production-scale AI project walkthrough
Select a past project where you moved an AI solution from pilot to production. Prepare to discuss the technical architecture, how you handled data privacy and security, and the specific metrics (e.g., efficiency, turnaround time) used to measure business impact.
Demonstrate RAG and LLM implementation expertise
Be ready to explain your approach to building RAG pipelines, including document ingestion, vector database selection, and prompt engineering strategies to minimize hallucinations in a regulated financial or insurance context.
Review Microsoft AI and automation ecosystem
Familiarize yourself with Azure AI services, Copilot Studio, and the Power Platform. Prepare examples of how you have integrated these tools with existing enterprise workflows or document repositories to solve business problems.
Develop a framework for scaling AI solutions
Create a brief outline of how you design reusable solution patterns. Explain your methodology for ensuring that an AI tool developed for one team can be effectively scaled across global operations while maintaining governance and compliance.
Formulate strategic questions for the hiring team
Prepare questions regarding the current maturity of the CIMS organization's AI infrastructure, how they balance pro-code versus low-code development, and the specific challenges they face in scaling AI within their regulated insurance environment.

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