Location: [Chennai/Hyderabad/Bangalore (Hybrid)]

About the Role

We are seeking a seasoned Enterprise GenAI Solution Architect with 12+ years of experience leading complex platform engineering, distributed system design, and enterprise AI transformation initiatives.

This is a forward-deployed, hands-on architecture role. You will work directly with enterprise customers on multiple proof-of-concept and modernization engagements, and contribute to building our next-generation agentic AI platforms. You will partner with C-suite executives, enterprise stakeholders, and engineering teams to design, govern, and scale enterprise-grade Generative AI solutions.

We are looking for a core technical leader who loves challenging engineering problems, enjoys solving them end to end, and can lead and grow a strong technical team. The ideal candidate has a proven track record of architecting multi-agent orchestration platforms, building hybrid Retrieval-Augmented Generation (RAG) architectures, and scaling full-stack platforms under real-world enterprise constraints — security, RBAC, context management, governance, and operational reliability.

Key Responsibilities

Strategic AI Architecture & Orchestration

- Design and implement enterprise-grade GenAI platforms, multi-agent frameworks, and autonomous agent execution systems for complex workflow automation.
- Architect advanced RAG pipelines incorporating hybrid search, vector databases, knowledge graphs, and enterprise knowledge retrieval systems.
- Lead context engineering strategies — state management, memory orchestration, and context graphs — across multi-model GenAI ecosystems.
- Design LLM gateway and model-routing layers: multi-provider access, model tiering, guardrails, policy enforcement, and evaluation.
- Establish LLM observability — tracing, evaluation, cost tracking, and quality monitoring — for agentic systems.
- Drive AI-assisted Software Development Life Cycle (SDLC) transformations and automated code modernization frameworks.

Distributed Systems & Full-Stack Integration

- Architect resilient, event-driven, microservice-based backend platforms using Python (FastAPI), Node.js, or Java, with message brokers and task queues such as Kafka, RabbitMQ, or Celery/Redis.
- Oversee modern,



scalable web applications built on React.js, Next.js, or Angular with TypeScript.
- Design secure, isolated sandbox execution environments for agent-generated code and tool execution.
- Build agent tool integrations using standards such as the Model Context Protocol (MCP) to connect agents with enterprise systems.
- Ensure seamless enterprise security and identity integration across all AI architectures — OAuth2, JWT, RBAC, SSO (Auth0, Keycloak) — and multi-tenant isolation.

Cloud & Platform Engineering

- Design cloud-native AI infrastructures across AWS, Azure, and GCP, utilizing managed AI services such as AWS Bedrock, Azure OpenAI, or Vertex AI.
- Establish containerized execution environments using Docker and Kubernetes for scalable, high-throughput AI inference and data pipelines.
- Apply infrastructure-as-code (Terraform / CDKTF), CI/CD, and DevOps/MLOps best practices.
- Set standards for observability, performance optimization, model evaluation, security, and cost-effective cloud resource usage.

Client Engagement & Technical Leadership

- Lead customer-facing technical workshops, architecture review boards (ARBs), and AI governance alignment sessions with enterprise clients.
- Own multiple customer POCs in parallel — translating complex business problems into scalable, production-ready solutions and enterprise roadmaps.
- Lead and mentor a strong technical team on AI systems design, orchestration patterns, distributed architectures, and software engineering best practices.

Required Qualifications & Expertise

- Overall Experience: 12+ years of progressive experience in software engineering, system design, and enterprise solution architecture.
- GenAI & Agentic Systems (minimum 2+ years dedicated hands-on experience):
- Production experience building with multi-agent orchestration frameworks such as LangGraph, CrewAI, LangChain, AutoGen,



or Microsoft Agent Framework / Agent SDK.
- Deep experience with LLM integration, prompt design, fine-tuning methodologies, context management, and guardrails.
- Demonstrated expertise in vector search and enterprise retrieval engines such as Pinecone, OpenSearch, Milvus, ChromaDB, or Weaviate.
- Experience with graph databases (e.g., Neo4j) for knowledge graphs and context management is a strong plus.
- Full-Stack & Backend Engineering:
- Frontend: Hands-on mastery in modern enterprise web architecture using React, Next.js, or Angular with TypeScript.
- Backend: Advanced capability in Python (FastAPI/Flask), Node.js, or Java microservices, including async task processing (Celery, Redis).
- Databases: Mastery of relational and NoSQL databases (PostgreSQL, MongoDB, Redis, DynamoDB).
- Cloud & Distributed Architecture:
- Proven track record designing cloud-native solutions across major cloud environments (AWS, Azure, GCP).
- Solid foundation in event-driven systems (Kafka, RabbitMQ), distributed systems, and API design.
- Hands-on familiarity with Docker, Kubernetes, CI/CD pipelines, IaC, and DevOps/MLOps best practices.
- Security & Best Practices: Strong exposure to application security, identity (OAuth2, JWT, SSO, RBAC), secure sandboxing, and engineering best practices.
- Client & Stakeholder Mastery: Exceptional consultative, communication, and executive presentation skills.
- Mindset: A hands-on builder who thrives on challenging technical problems and takes ownership from design through delivery.

Preferred Qualifications

- Master's degree in Computer Science, Software Engineering Management, or a related field.
- Specialized certifications in Cloud and AI architectures (e.g., AWS Certified Solutions Architect, Claude Certified Architect – Foundations, or Azure AI Engineer).
- Experience building or operating an LLM gateway, model router, or AI evaluation/policy engine.
- Published technical articles, patent filings, or active speaking experience on Multi-Agent Systems, RAG Architectures, or Enterprise AI Transformation.
- Experience driving AI adoption in legacy code migrations, dependency extractions, or complex enterprise digital modernizations.

📌 Enterprise GenAI Solution Architect (Chennai)
🏢 Galent
📍 Chennai