Role Overview

We are seeking an experienced GenAI Architect to define and drive the architecture of enterprise-grade Agentic AI platforms and cloud-native solutions. The ideal candidate will have deep expertise in LLM-based systems, RAG architectures, AI agent frameworks, vector search, Python-based services, and cloud platforms.

The role involves translating complex business requirements into secure, scalable, resilient, and production-ready AI architectures while providing technical leadership throughout the solution lifecycle.

Roles & Responsibilities

• Define end-to-end architecture for enterprise GenAI platforms, Agentic AI applications, and multi-agent systems.
• Design LLM-powered solutions incorporating RAG, prompt engineering, context management, and tool calling.
• Architect AI agents and workflows using LangChain, LangGraph, and related frameworks.
• Design vector search and retrieval architectures for semantic search, contextual grounding, and response relevance.
• Establish architectural standards for Python, FastAPI, REST APIs, and microservices.
• Evaluate and implement cloud-native AI solutions across Microsoft Azure, AWS, and Google Cloud Platform (GCP).
• Design containerized deployment architectures using Docker and Kubernetes.
• Define reference architectures, reusable components, integration patterns, and engineering best practices.
• Address scalability, availability, security, privacy, latency, observability, and cost optimization.
• Establish responsible AI governance, data protection, access controls, and LLM application security practices.
• Evaluate LLMs, embedding models, vector databases, orchestration frameworks, and emerging AI technologies.
• Lead architecture reviews, technical decision-making, and production deployment strategies.
• Collaborate with business stakeholders, product teams, and engineering leadership to define AI roadmaps and solution strategies.

Mandatory Skills

• Agentic AI
• Large Language Models (LLMs)
• Python Programming
• FastAPI Development
• RAG Architecture
• Vector Search
• LangChain / LangGraph
• Cloud Platforms (Azure/AWS/GCP)
• Docker Containers
• Kubernetes Orchestration

Preferred Qualifications

• Bachelor's or master's degree in Computer Science, Information Technology, or a related field.
• Proven experience designing and deploying enterprise-scale GenAI solutions.
• Understanding of cloud security, identity and access management, networking, and enterprise integration.
• Knowledge of LLM evaluation, guardrails, hallucination mitigation, model selection, and AI governance.
• Experience defining technical roadmaps, conducting architecture reviews, and mentoring senior engineering teams.
• Familiarity with CI/CD, monitoring, observability, deployment automation, MLOps, and LLMOps.

Why Join Us?

• Architect next-generation enterprise GenAI and Agentic AI solutions.
• Drive technology strategy and architectural decisions for cloud-native AI platforms.
• Work with advanced LLMs, multi-agent systems, RAG, and vector search technologies.
• Solve complex enterprise challenges involving scalability, security, and performance.
• Lead technical innovation and influence end-to-end AI solution delivery.

Ideal Candidate Profile

A strategic and hands-on technology architect who can define enterprise GenAI architecture, make sound technology decisions, and guide engineering teams in delivering secure, scalable, cloud-native Agentic AI solutions from design through production.

Application Instructions: Interested candidates can apply with an updated resume highlighting their enterprise GenAI architecture experience, Agentic AI expertise, RAG implementations, vector search, and cloud-native deployments.