Principal Architect

Area(s) of responsibility Key Job Responsibilities

Architecture & Design

Define end-to-end AI/GenAI architecture for enterprise applications

Design scalable solutions using LLMs, RAG pipelines, vector databases, and agentic workflows

Azure AI Platform

Architect solutions using Azure OpenAI, Azure ML, Databricks, AI Search

Design cloud-native architectures (scalability, HA/DR, cost optimization)

Development & Engineering

Lead hands-on Python development for AI/ML and GenAI applications

Build APIs, microservices, and reusable AI components

GenAI Implementation

Implement:

RAG (retrieval-augmented generation) pipelines

Vector search and embeddings

Multi-agent and tool-calling frameworks

Develop AI copilots, chatbots, and automation systems

Governance & Security

Ensure Responsible AI, compliance, and data governance (PII, lineage, security)

Implement guardrails: prompt security, content filters

Leadership & Collaboration

Provide architecture guidance and mentor engineering teams

Work with business stakeholders to translate requirements into solutions

Mandatory Skills

AI/ML & GenAI

LLMs (GPT or open-source), RAG, embeddings, vector databases

Experience building enterprise GenAI solutions

LangChain, LangGraph

Agentic AI

Programming

Strong expertise in Python (mandatory)

Experience with APIs, microservices

Azure Cloud

Hands-on with:

Azure OpenAI / AI Studio

Azure ML, Databricks, Synapse

Storage (Blob, Cosmos DB), AI Search

Architecture

Experience designing scalable, distributed systems

Strong knowledge of cloud-native architecture patterns

Experience

10+ years in IT

5+ years in AI/ML

2+ years in Generative AI / LLM-based systems

Preferred Skills

Experience with:

LlamaIndex, Autogen, CrewAI

Multi-agent systems

Exposure to:

Vector DBs

Data platforms

Cloud & Platform:

Multi-cloud (AWS/GCP)

Kubernetes, containers

Governance:

Data governance tools (Purview, cataloging, lineage)

Certifications:

Azure Solutions Architect / Azure AI Engineer