AI Architect

15 years overall; 3–5 years architecting AI/ML/GenAI solutions at scale.

Role Summary
We are seeking an AI Architect with deep hands-on experience in AI/ML and Generative AI to design and govern end-to-end solutions across application, data, and infrastructure layers. The Architect will own reference architectures, lead solution shaping, and ensure responsible AI practices across discovery, build, and run—covering LLM/RAG, agentic AI, LLMOps/MLOps, and cloud-native delivery.

Key Responsibilities
Architecture & Design
Define and evolve reference architectures for AI-powered solutions spanning data ingestion → model orchestration → inference services → application integrations; ensure scalability, security, and observability.

Select and integrate LLMs (Azure OpenAI, Google, AWS) and design RAG pipelines with vector stores (e.g., Pinecone, FAISS, Azure Cognitive Search) and prompt strategies; optimize for latency/cost/accuracy.

Architect agentic AI components (multi-agent workflows, gateways, registries, protocol interoperability) for complex enterprise use-cases; establish guardrails and governance.
Lead solution reviews, threat modeling, and Responsible AI compliance (privacy, bias, explainability, auditability).

Build, Integrate & Automate
Guide engineering on LLMOps/MLOps (model lifecycle, feature stores, CI/CD for ML, telemetry, A/B & canary releases) and integrate with DevOps toolchains; automate infra as code and pipelines for AI services.

Orchestrate data and model services via microservices, event-driven patterns, and secure APIs; design plugins/connectors for Copilot/Power Platform when applicable.

Stakeholder Engagement & Governance
Translate business goals into AI solution roadmaps; run discovery workshops, backlog shaping, and architecture runway planning with product, data, and platform teams.
Establish toll-gates, NFRs, and KPIs for cost, quality, and time-to-value; drive continuous improvements through retrospectives and RCA across baseline/trial/test phases.

Required Skills & Experience
Core Technical
· 15 years overall; 3–5 years architecting AI/ML/GenAI solutions at scale.

Hands-on with LLMs (GPT family, BERT/Transformers), prompt engineering, LLM optimization (context windows, adapters), RAG, and evaluation (golden sets, BLEU/BERTScore/semantic similarity). 

Cloud: Azure (incl. Azure AI, OpenAI, Cognitive Services), AWS (Bedrock/SageMaker), Google Cloud Platform (Vertex AI); containerization (Docker/Kubernetes) and service meshes. 

Data & Integration: SQL, NoSQL, data warehousing concepts (fact/dimension, SCD), ETL/ELT, streaming (Kafka/Event Hubs), API design, and secure integration (OAuth2/JWT/RBAC).
 LLMOps/MLOps & Automation: CI/CD for models, model registry, feature store, monitoring (tokens, drift, hallucinations), Python automation for infra and pipelines. Engineering Practices 
Proficiency in Python, LangChain/LangGraph, vector DBs, and prompt/agent frameworks; strong Git, CI/CD, IaC, and Agile/Scrum delivery.

Architecture Leadership · Prior ownership of complex AI solution designs, HLD/LLD, NFRs, and security/ compliance patterns; ability to mentor squads and lead design forums. Preferred Skills · Experience designing AI gateways, MCP (Model Context Protocol) implementations, and agent registries; familiarity with metadata standards (DCAT/JSON-LD).

 Exposure to enterprise AI platforms/labs and accelerators · Knowledge of infrastructure automation in DC/cloud (orchestration, provisioning) with AI workloads and GPU scheduling (K8s, NCCL, Triton).

 Prior work on Microsoft Copilot Studio solutions, conversational agents, and Power Platform extensibility. 

Qualifications & Certifications · B.Tech or Equivalent degree · Certifications (any subset): Azure AI Engineer/Architect, Azure OpenAI, AWS/Google Cloud Platform AI, Databricks ML, Kubernetes

Salary : $60 - $70