About the role
Role Overview
This role owns the technical architecture of the Data & AI product portfolio and acts as the Technical counterpart to the Product Owner. Works with Innovation on PoC-to-product transitions and applies AI governance and Responsible AI standards. Responsible for architecting products for multi-tenancy and non-functional characteristics.
Key responsibilities
Product architecture & portfolio
Own the target architecture for each product in the portfolio, designing for reuse, configurability, and multi-tenancy.
Define the shared platform layer — common services, components, and infrastructure reusable across products — and drive consolidation of duplicated capability.
Own architecture roadmap in sync with product roadmap.
AI & GenAI solution design
Responsible for design decisions: build vs. buy, model selection, fine-tuning vs. retrieval, technical debt management and trade-offs.
Design retrieval architectures (chunking, embedding, indexing, hybrid search, and re-ranking) for accuracy and latency at scale.
Define the data architecture for AI solutions — training data, feature pipelines etc.
Architect observability, monitoring, and drift detection
Product maturation and disposition
Assess PoCs and early MVPs for technical viability, scalability, and productisation.
Conduct product technical readiness assessments; provide the architectural basis for product disposition decisions.
Production readiness & unit economics
Design the LLMOps/MLOps backbone for the portfolio — serving, versioning, deployment pipelines, and rollback.
Define evaluation architecture: eval harnesses, quality benchmarks, regression testing.
Own the cost model — inference economics and token cost — as an input to product pricing.
Safety, governance & technical leadership
Embed Responsible AI, explainability, and AI security standards into product designs. Design for the regulatory obligations.
Support the Product Owner with technical input to funding pitches, business cases, and customer conversations.
Experience
10–15 years in software/ML engineering and architecture, with proven AI solutions in production
Expected Skills
GenAI architecture — LLMs, RAG, agentic patterns, orchestration frameworks
MLOps/LLMOps, evaluation design, model serving, observability, and inference cost optimisation
Experience in architecting commercial software products - Multi-tenancy, versioning, and productisation of prototypes
Working knowledge of data platforms, pipelines, and modelling
Ability to connect architectural choices to unit economics, profitability and time-to-market
Guide the product development teams with implementation and standards compliance
Educational qualification:
B.E/B.Tech/MCA/Phd or equivalent Qualification
Experience :
10–15 years in software/ML engineering and architecture, with proven AI solutions in production
Mandatory/requires Skills :
GenAI architecture — LLMs, RAG, agentic patterns, orchestration frameworks
MLOps/LLMOps, evaluation design, model serving, observability, and inference cost optimisation
Experience in architecting commercial software products - Multi-tenancy, versioning, and productisation of prototypes
Working knowledge of data platforms, pipelines, and modelling
Ability to connect architectural choices to unit economics, profitability and time-to-market
Guide the product development teams with implementation and standards compliance
Preferred Skills :