Enterprise GenAI Solution Architect

Galent; Chennai District; On-site; INR 4,500,000 - 7,500,000; Full time

Job summary

Galent in Chennai, India, seeks a seasoned Enterprise GenAI Solution Architect with 12+ years of experience to lead complex platform engineering, distributed design, and enterprise AI transformation initiatives. This is a forward‑deployed, hands‑on role working with enterprise customers on proof‑of‑concepts and modernization engagements, partnering with C‑level stakeholders to scale agentic AI platforms.

You will mentor a technical team and drive security, RBAC, context management, and

Qualifications

• 12+ years of progressive software engineering, system design, and enterprise solution architecture.
• The candidate must have 2+ years of hands-on GenAI/agentic experience.
• Experience building multi-agent orchestration frameworks and RAG pipelines with enterprise-grade requirements.

Responsibilities

• Design and implement enterprise‑grade GenAI platforms and multi‑agent orchestration systems.
• Architect advanced RAG pipelines and integration with enterprise knowledge retrieval systems.
• Lead context engineering strategies and establish LLM observability and governance.
• Drive AI‑enabled SDLC transformations and automated code modernization frameworks.

Skills

GenAI architecture

Agent orchestration

LLM integration

Distributed systems design

Security & RBAC

Cloud architecture

Team leadership

Client engagement

Problem solving

Coding proficiency

Education

Master's degree in Computer Science or related field

Tools

Docker

Kubernetes

Terraform

CI/CD

LangChain / LangGraph

OpenAI APIs

Vector databases

Kafka

Job description

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.
• 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.
• 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).
• Strong 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.