Product Architect

E-Solutions; Dadri; On-site; INR 4,000,000 - 7,000,000; Full time

Job summary

E-Solutions in India is seeking a visionary leader to own the end-to-end product architecture and technology roadmap for a cutting-edge enterprise platform. You will define scalable, secure architectures and translate business needs into concrete technical blueprints, including AI/GenAI capabilities.

Lead multi-disciplinary teams across AI/ML, product, and engineering, driving API-first design, microservices, and cloud-native patterns while ensuring performance, security, and governance across

Qualifications

• Candidates should demonstrate expertise in scalable distributed architectures for enterprise SaaS platforms.
• Experience with AI/GenAI architectures, LLMs, and knowledge-based AI is desirable.
• Strong background in product strategy, Agile/ Scrum, and delivering multi-team roadmaps.

Responsibilities

• Own end-to-end product architecture and technology roadmap.
• Translate business requirements into product capabilities and solution blueprints.
• Design AI/GenAI architectures including LLMs, RAG, NLP, and conversational AI.
• Drive API-first, microservices and cloud-native architectures.
• Ensure scalability, availability, observability, and security.

Skills

Distributed systems

Microservices

REST APIs

Event-driven

Kubernetes

CI/CD

DevSecOps

Observability

SRE principles

Enterprise integration

Tools

Kubernetes

API gateways

Vector databases

Job description

• Own the end-to-end product architecture and technology roadmap.
• Define scalable, secure, modular and enterprise-grade architectures.
• Translate business requirements into product capabilities, technical architecture and solution blueprints.
• Design AI/ML architecture covering LLMs, GenAI, RAG, NLP, conversational AI, multimodal AI, AI agents and AI evaluation.
• Drive API-first, microservices, event-driven and cloud-native architecture.
• Ensure scalability, availability, reliability, observability, performance and maintainability.
• Define integration architecture across internal platforms, external products and third-party services.

2. Product Management

• Translate customer and business requirements into product capabilities, roadmap and technical priorities.
• Drive product backlog, feature prioritization and release planning with Product and Business teams.
• Perform competitive benchmarking and product capability assessment.
• Identify product gaps and define capabilities required to make products enterprise-ready and market competitive.
• Drive Build vs. Buy vs. Integrate decisions based on business value, technical feasibility, cost, scalability and time-to-market.
• Participate in customer discussions, discovery sessions and product demonstrations.

The candidate should have a strong understanding of:

• Generative AI and LLM-based applications
• AI Agents and Agentic workflows
• RAG and knowledge-based AI
• NLP and conversational AI
• AI-based recommendations and decisioning
• AI evaluation, benchmarking and quality frameworks
• Human-in-the-loop AI
• Model selection, orchestration and optimization
• AI governance, responsible AI and model monitoring

4. Enterprise Platform Capabilities

Drive architecture and productization of:

• Multi-tenancy and tenant isolation
• RBAC and enterprise identity integration
• API gateway and partner integrations
• Workflow and orchestration engines
• Analytics, dashboards and reporting
• Audit trails and data lineage
• Consent, privacy and data retention
• Security and compliance
• Monitoring, observability, SLA and production support architecture
• High availability, disaster recovery and business continuity

5. Product Engineering Leadership

• Work closely with Engineering, AI/ML, UX, QA, DevOps, Security and Product teams.
• Establish architecture standards, design principles and technical governance.
• Review technical designs and ensure alignment with product strategy and roadmap.
• Mentor architects, senior engineers and technical leads.
• Drive technical debt reduction, modernization and platform scalability.
• Ensure engineering execution remains aligned with product outcomes.

Required Skills

• Distributed systems and microservices
• REST APIs, event-driven architecture and API gateways
• Databases, caching, messaging and data platforms
• Kubernetes and containerization
• CI/CD, DevSecOps, observability and SRE principles
• Enterprise integration and solution architecture

AI / GenAI

• LLMs and Generative AI
• RAG and vector databases
• AI Agents / Agentic AI
• NLP and conversational AI
• AI evaluation and benchmarking
• Model governance and responsible AI
• AI/ML lifecycle and MLOps

Data

• Data architecture and data engineering
• Structured and unstructured data
• Data pipelines and data processing
• Data quality, lineage and governance
• Real-time and batch processing
• Knowledge graphs / vector search is desirable

Product Management

• Product strategy and roadmap
• Product backlog and prioritization
• Agile / Scrum
• Customer discovery and requirement analysis
• Competitive analysis
• Business case and ROI evaluation

Preferred Experience

• Experience building and scaling enterprise SaaS, AI/ML, GenAI or platform products.
• Experience working across Product, Engineering, AI/ML and Business functions.
• Experience with complex enterprise integrations and third-party ecosystems.
• Experience evaluating competing products and defining product differentiation and technology strategy.
• Experience in multiple business domains is preferred, enabling the candidate to architect and productize solutions across different industries.

Key Success Measures

The person should be able to:

• Define and own the product and technology roadmap.
• Convert business problems into scalable product and technology solutions.
• Identify and leverage the right AI/GenAI capabilities to enhance products.
• Establish strong security, privacy, governance, reliability and observability practices.
• Identify competitive gaps and convert them into prioritized product capabilities.
• Reduce unnecessary technology and vendor dependencies through platform reuse and standardization.
• Work effectively across Product + Engineering + AI/ML + Data + Business teams.
• Drive products from strategy and architecture through execution and production scale.