Solutions Architect - Generative AI Platform (4-7 yrs)

Position : Solution Architect / Technical Lead GenAI & Data Products.

Location : Gurgaon Hybrid || Immediate Joiners Preferred.

Were looking for a highly skilled and hands-on Solution Architect or Technical Owner who excels in designing and delivering Data and AI-powered platforms, with a strong focus on cloud-native applications, machine learning, and Generative AI (GenAI).

This role combines deep technical knowledge with a product mindset, where you'll drive the design and development of resilient, scalable, and secure systems from the ground up.

Role Summary :

As a Tech Lead, you will guide cross-functional teams in developing modern AI products leveraging AWS cloud, ML pipelines, and GenAI integration (LLMs, RAG).

You'll be responsible for solution design, infrastructure setup, full-stack delivery, and governance enforcement.

Key Responsibilities :

- Architect and deliver GenAI solutions using AWS tools such as ECS, RDS, Lambda, and S3.

- Build scalable, modular microservices using Python, integrating Redis for caching and Qdrant/Redis Vector for semantic search.

- Incorporate GenAI tools and frameworks like OpenAI, LangChain, LangGraph, and HuggingFace APIs with secured and optimized API pipelines.

- Lead the frontend stack using Next.js (TypeScript) for highly responsive, server-side rendered interfaces.

- Manage cloud infrastructure automation through Terraform, AWS CDK, and GitHub Actions in CI/CD pipelines.

- Design data systems combining Snowflake, PostgreSQL (RDS), and S3, ensuring performance and reliability for analytics and transactional needs.

- Support legacy-to-cloud transformations and enforce best practices in security, compliance, and operational readiness.

- Drive team excellence through mentorship, code/design reviews, and collaborative development aligned with business goals.

Ideal Candidate Profile :

- Extensive experience leading AI/ML platform development and integrating Generative AI solutions.

- Deep expertise in AWS architecture and end-to-end product engineering.

- Strong familiarity with machine learning operations, LLM productionization, and vector databases.

- Experience with frontend/backend orchestration, DevOps tools, and secure cloud deployment.

- Proven ability to collaborate with stakeholders, define technical strategy, and mentor engineering teams.