Senior Solution Architect - Generative AI Platform

We are seeking a hands-on Solution Architect / Tech Owner with deep expertise in building and scaling Data & AI products end-to-end.

This role demands a strong foundation in cloud-native architecture (AWS), machine learning (ML), LLM-based GenAI integration, and a design thinking mindset to translate business needs into resilient, high-performance solutions.

The ideal candidate has led the technical delivery of scalable GenAI platforms, architected modular backend systems and frontend interfaces, and built ML pipelines in production environments.

You will be responsible for owning system design, guiding technical teams, deploying full-stack infrastructure, and ensuring robust governance and security across all layers.

Key Responsibilities :

• Architect and deliver end-to-end GenAI platforms using AWS (ECS, RDS, Lambda, S3) with real-time LLM orchestration and RAG workflows.
• Design and implement Python microservices with Redis caching and vector search using Qdrant or Redis Vector.
• Integrate GenAI models and APIs (OpenAI, HuggingFace, LangChain,LangGraph), including containerized inference services and secured API pipelines.
• Lead frontend architecture using Next.js (TypeScript) with SSR and scalable client-server routing.
• Own infrastructure automation and DevOps using Terraform, AWS CDK, GitHub Actions, and Docker-based CI / CD pipelines.
• Manage and optimize data architecture across Snowflake, PostgreSQL (RDS), and S3 for both analytical and transactional needs.
• Knowledge of database pipeline and data quality, transitioning legacy systems to modular, cloud-native deployments.
• Champion engineering culture, leading design / code reviews, mentoring team members, and aligning technical priorities with product strategy.
• Ensure compliance, encryption, and data protection via AWS security best practices (IAM, Secrets Manager, WAF, API Candidate Profile :
• Proven track record as a Solution Architect / Tech Lead on large-scale Data & AI products with GenAI integration.
• Deep knowledge of AWS cloud services, microservices architecture, and full-stack deployment.
• Strong understanding of ML lifecycle and productionization of LLMs / GenAI APIs.
• Practical experience with design thinking, breaking down problems from user need to system delivery.
• Excellent leadership, communication, and mentoring skills to drive team alignment and technical execution.

(ref : hirist.tech)