Associate Architect

AXA Global Business Services Pvt. Ltd.; Bengaluru, Pune District; On-site; INR 4,000,000 - 7,000,000; Full time

Job summary

PeopleStrong is seeking an AI Product Lead / AI Delivery Lead in Bengaluru, India. You will own AI initiatives from business case evaluation to production deployment, monitoring, and adoption, leading cross-functional teams across Data, AI, Tech, and Biz.

Strong emphasis on MLOps, Azure/Databricks stacks, and ROI-driven delivery. Role requires 8–10 years in Data/AI/ML with 2–3 years in AI leadership, and the ability to translate business problems into scalable AI solutions for the insurance

Qualifications

• 8–10 years in Data, AI and ML with leadership experience.
• Experience guiding AI projects to production end-to-end.
• Strong stakeholder management and communication skills.

Responsibilities

• Lead AI projects from business case to production delivery.
• Define roadmaps, KPIs and ROI for AI use cases.
• Collaborate with business stakeholders to translate problems into AI solutions.
• Guide AI engineers, data scientists, and MLOps teams; review designs and pipelines.
• Ensure MLOps/AIOps practices for deployment, monitoring and incident management.
• Drive adoption, performance, and scale-up of AI solutions.

Tools

Databricks

MLFlow

Azure ML

OpenShift

Azure DevOps

Jenkins

Job description

Job Description

JOB DESCRIPTION

Date: 23/09/2026

Job Title: AI Product Lead / AI Delivery Lead

Join us as an AI Product Lead / AI Delivery Lead within the AI Engineering division of DAAS (Data, AI & Automation Services). This is a tech?lead type of role where you will take end?to?end ownership of AI initiatives—from business case evaluation and roadmap definition to production deployment, monitoring, and adoption.

In this role, you will:

• Lead and deliver AI projects to production end?to?end, ensuring alignment with business expectations and strategic priorities across the insurance value chain
• Define project roadmaps, success metrics, and KPIs (business, technical and operational) for AI use cases, and track them throughout the lifecycle
• Evaluate and prioritize business cases for new AI requirements, assessing feasibility, expected impact, and ROI
• Work closely with business stakeholders to translate business problems into AI solutions and ensure that delivered products meet value, quality, and time?to?market expectations
• Oversee the implementation of AI solutions by guiding AI engineers / data scientists / MLOps engineers, while remaining hands?on enough to review designs, pipelines and model performance
• Ensure robust MLOps / AIOps practices for model deployment, monitoring, incident management, and continuous improvement in production environments
• Assess and drive the performance, efficacy, and adoption of AI solutions, and identify opportunities for optimization or scale?up
• Collaborate with platform and DevOps teams to ensure AI solutions are deployed using best practices on Azure, Databricks, OpenShift, MLFlow, and/or Azure ML
• Contribute to AI product strategy, delivery best practices, and reusable frameworks within DAAS

If you are an AI professional with solid delivery experience, passionate about turning AI use cases into high?impact, production?grade solutions in insurance, this is an opportunity to lead and make a significant impact.

Technical skills:
Expertise is required in the following fields:

• End?to?end delivery of at least 3–5 AI projects taken to production, with concrete ownership of planning, execution, and post?production monitoring
• Practical experience with AIOps and MLOps concepts and tooling (e.g., Databricks, MLFlow, Azure ML, OpenShift) in real?world deployments
• Strong understanding of CI/CD practices for ML, including setting up or working with pipelines using Azure DevOps, Jenkins, etc., for model deployment and updates
• Ability to review and guide solution architectures for AI systems (data pipelines, model training, serving, monitoring)
• Hands?on experience with Generative AI technologies – LLMs, RAG, Prompt Engineering – and their application to business use cases is a strong plus
• Broad understanding of machine learning algorithms and techniques, including LLMs/SLMs, supervised/unsupervised learning, and agentic AI, sufficient to challenge and guide technical teams
• Experience defining and monitoring KPIs/SLAs for AI systems (e.g., model performance, latency, drift, adoption, business impact)
• Ability to assess data readiness, technical feasibility, and integration constraints for proposed AI use cases
• Proven track record of leading cross?functional teams (Data, AI, Tech, Business) to deliver AI initiatives on time and within scope
• Ability to work collaboratively with broader Data and Tech teams, along with global multi?lingual teams
• Strong stakeholder management skills; able to align diverse stakeholders and manage expectations across business and IT
• Excellent communication skills to:
  • Collect and challenge requirements from non?technical stakeholders
  • Explain AI concepts, risks, and trade?offs in clear business language
  • Communicate project plans, deliverables, value generated, and challenges/risks
• Strong sense of ownership and accountability for delivery and outcomes
• Structured, outcome?oriented mindset with the ability to prioritize and manage multiple initiatives in parallel
• Strong problem?solving and decision?making skills, including the ability to balance business impact, complexity, and risk

Background and experience:

• 8–10 years of progressive experience in Data, AI and ML, including:
  • Minimum 2–3 years specifically in AI project or product leadership (AI Product Lead, AI Delivery Lead, Tech Lead, or similar role)
  • Demonstrated experience leading and delivering multiple AI projects to production end?to?end
• Experience in deploying and managing AI models in production environments using Databricks, MLFlow, Azure ML, OpenShift, or similar platforms
• Experience evaluating business cases for AI use cases, including estimation of value/ROI, complexity, and risk
• Experience in assessing and improving performance, efficacy, and adoption of AI solutions post?deployment