Role Overview

As an AI Platform Architect, you will design and scale enterprise-grade Agentic AI and Generative AI platforms that power copilots, autonomous workflows, and multi-agent systems. This is a hands-on architect role for a builder who combines deep distributed-systems thinking with practical expertise in LLMs, agent orchestration, and cloud-native platforms.

You will define reference architectures, engineering standards, and core platform capabilities that enable teams to build, deploy, and operate AI systems at scale.

Key Responsibilities

Platform & Architecture Leadership

• Architect scalable Agentic AI platforms supporting multi-agent orchestration, tool usage, memory, planning, and reasoning workflows
• Design end-to-end GenAI platforms, including:
  • Prompt orchestration
  • Retrieval-Augmented Generation (RAG) pipelines
  • Vector databases
  • LLM integrations
• Define modular, microservices-based, API-first architectures enabling rapid development of AI copilots and autonomous systems

Agentic & GenAI Systems

• Define architectural patterns for:
  • Agent orchestration and coordination
  • Planning, reflection, and reasoning loops
  • Human-in-the-loop and feedback-driven systems
• Evaluate and integrate emerging frameworks such as LangChain, LlamaIndex, AutoGen, CrewAI, and related ecosystems into enterprise platforms

Production Readiness & Reliability

• Ensure production-grade reliability across AI platforms, including:
  • Observability and monitoring
  • Evaluation and experimentation frameworks
  • Guardrails, safety, and governance
  • Cost optimization for LLM-based systems
• Lead architecture across LLMOps, MLOps, and platform operations

Cloud, DevOps & Infrastructure

• Drive cloud-native platform architecture using AWS, Azure, or GCP
• Lead Kubernetes-based deployments, CI/CD pipelines, and Infrastructure as Code (IaC)
• Ensure scalability, security, and resilience of AI workloads

Collaboration & Technical Leadership

• Partner with data science, product, and business teams to translate AI use cases into robust platform capabilities
• Mentor senior engineers and architects; set engineering standards, reference architectures, and best practices
• Act as a technical thought leader for enterprise AI platform strategy

Required Skills & Experience

• 12–15 years of experience in platform engineering / architecture, with strong depth in distributed systems
• Deep expertise in cloud platforms (AWS / Azure / GCP) and Kubernetes-based architectures
• Hands-on experience with LLMs, RAG architectures, and vector databases (e.g., Pinecone, FAISS, Weaviate)
• Strong understanding of agentic frameworks, multi-agent systems, and orchestration patterns
• Experience across LLMOps / MLOps, including:
  • Model deployment
  • Monitoring and evaluation
  • Lifecycle and version management
• Proficiency in microservices, APIs, event-driven systems, and scalable backend engineering
• Strong coding experience in Python / Java / Go, with system-design depth
• Experience working with data platforms (real-time + batch, large-scale data processing)
• Solid understanding of AI safety, governance, and responsible AI frameworks

Good to Have

• Experience building enterprise AI copilots or autonomous workflows
• Exposure to reasoning frameworks, chain-of-thought orchestration, and agent memory systems
• Prior consulting or client-facing experience leading AI-driven transformations

What We’re Looking For

A builder–architect who combines deep engineering rigor with a strong grasp of Agentic and Generative AI—someone who can turn cutting-edge AI concepts into scalable, secure, and production-ready enterprise platforms.