01Overview
Description
What you can look forward to as an Architect Data & AI
End to End Architecture Ownership: Own architecture and design of production grade Data & AI platforms spanning cloud infrastructure (Azure), data platforms, ML/LLM systems, and agentic workflowsfrom concept through deployment, operation, and evolution.
Agentic AI Systems (Production First): Architect and drive AI agents using LangGraph, LangChain Expression Language (LCEL), Semantic Kernel, or similar frameworks, emphasizing context engineering, tool routing, memory strategies, guardrails, and deterministic workflows over prompt hacks.
Context & Retrieval Engineering: Design robust RAG and non RAG context pipelines using vector databases (Azure AI Search, Pinecone, Weaviate, pgvector), embeddings lifecycle, chunking strategies, re ranking, caching, and fallback logic that work at scale.
Model & Agent Evaluation: Define and operationalize agent evaluation frameworks (offline + online), including regression tests, task success metrics, hallucination detection, latency/cost controls, and MCP style orchestration patterns for controlled agent execution.
Data Platform Architecture: Architect data mesh aligned platforms with domain data products, Delta Lake, Databricks, Spark, event driven pipelines, relational and NoSQL databases, and clear ownership, governance, and interoperability models.
Production Deployment & Ops: Lead production deployments using Azure ML, Databricks, AKS, MLflow, CI/CD, feature stores, monitoring, logging, and incident ready observability for both ML models and AI agents.
Technical Leadership & Stakeholder Management: Act as a senior architect across teamsreview designs, mentor engineers, align with product and business stakeholders, and make explicit trade offs across cost, risk, performance, and maintainability. Your profile as an Architect Data & AI :
1112 years building Data & AI systems, with proven production deployments of ML and agent based AI systems (not PoCs or demos).
Strong hands on experience with Python, data tooling (Databricks, Spark, Delta Lake, Azure Data Factory), and cloud native architectures on Azure.
Demonstrated expertise in agent design patterns, context engineering, retrieval strategies, tool calling, memory management, and failure mode handling.
Solid grounding in data architecture and databases (SQL, NoSQL, lakehouse, vector stores), including experience in data mesh or domain oriented data platforms.
Experience operating AI systems in real environmentssecurity, access control, cost management, latency, reliability, and compliance are second nature.
Comfortable leading architecture forums, influencing technical direction, and working closely with product, business, and engineering leadership.
Pragmatic mindset: focused on what runs reliably in production, not chasing the latest Generative AI buzzwords. .
