Product AI Lead

Role & responsibilities

8+ years of experience in designing and implementing modern Data, Machine Learning & LLM Based Agentic solutions, with at least 5 years in a technical lead or architect role for advanced analytics products

Strong expertise in GCP AI and data services such as Vertex AI, Big Query, workflows, Pub/Sub, AI APIs, Cloud Functions, Terraform etc.

Solid understanding of data architecture principles and hands-on experience collaborating with data engineers to design scalable data pipelines

Prior hands-on experience in software development (Python) and familiarity with modern full-stack frameworks while having experience to leverage coding agents like CodeX/Claude Code/Copilot to hasten coding.

Deep knowledge of machine learning workflows, MLOps patterns, and production deployment architectures

Prior experience of defining & implementing GenAI solutions using LLMs, RAG, Vector Databases, agent frameworks and prompt engineering techniques

Experience defining and managing Git workflows, reviewing PRs, and ensuring code quality governance

Experience with CI/CD, Docker/containers, and automated testing for AI products Google Cloud Professional Cloud Architect & Machine Learning Engineer certification is a plus

Proven ability to work cross-functionally with data engineers, data scientists, MLOps engineers, full stack developers, and product managers

Strong communication, documentation, and stakeholder management skills

Experience of delivering at least 2 advanced Analytics programs in the CPG / FMCG space

Preferred candidate profile

Design and own end-to-end AI product architecture leveraging GCPs AI and data services, ensuring scalability, performance, and cost efficiency

Collaborate closely with data scientists and MLOps teams to design scalable, production-ready ML code structures and efficient ML pipelines.

Partner with data engineers, acting as a data architect, to ensure data models, pipelines, and storage systems are optimized for AI workloads and adhere to architectural principles.

Support full stack developers by advising on infrastructure design, microservice architecture, and GCP implementation best practices.

Review code for architecture compliance, performance, and maintainability.

Promote best practices for DevOps/MLOps integration, testing, and continuous deployment

Define and enforce standards for system design documentation, review, and versioning

Ensure architectural artifacts are updated and aligned with enterprise blueprints

Collaborating with product managers to balance new features with refactoring efforts and establishing metrics to track technical debt reduction.

Rapid Prototyping – Create quick POCs with an idea of scaling it to a full-fledged Product

Stay abreast with emerging technologies and evolving best practices in AI, GenAI, and GCP to continuously modernize the product architecture