AIML Architect
Designs and governs end-to-end enterprise AI architectures spanning Generative AI, Agentic AI, RAG and ML — defining reference architectures, MLOps/LLMOps standards and Responsible AI governance while mentoring delivery teams. Core tech: LLMs (Azure OpenAI/OpenAI/Bedrock), LangChain/LangGraph, vector DBs, Python/FastAPI on Azure/AWS/GCP.
- Compensation
- Not disclosed
- Location
- Hyderabad
- Work setup
- Onsite
- Seniority
- Not specified
- Employment
- Full time
- Experience
- 11+ years
- Category
- Architecture
- Posted
Skills
Must have
- 11-15 years of experience
- MLOps (mandatory skill)
- GenAI/LLMs: Azure OpenAI, OpenAI, Anthropic, Gemini or AWS Bedrock; prompt engineering
- RAG & agentic frameworks: LangChain, LangGraph, Semantic Kernel, CrewAI, AutoGen; embeddings & vector DBs (Milvus, OpenSearch, pgvector)
- Programming & architecture: Python, API/microservices, FastAPI, JSON schema/Pydantic
- Cloud & data: Azure/AWS/GCP, MLOps fundamentals, SQL/NoSQL, data engineering
- Architecture leadership: reference architectures, scalability/security design, model governance
Nice to have
- Banking & Financial Services (BFSI), Wealth Management / Capital Markets experience
- AI-assisted software engineering, knowledge assistants and agentic workflows
Full description
Location: HyderabadAI Architect (Band 5.2, Level 1)Role Summary
We are seeking an AI Architect to design, standardize and govern end-to-end AI architectures that turn business goals into production-grade, secure and scalable AI solutions. The role spans data, model, application and infrastructure layers, providing architectural leadership across Generative AI, Agentic AI, RAG and ML initiatives with allocation shared across programs.
Key Responsibilities
- Define AI reference architecture and integration patterns aligned to enterprise data strategy, security and cost constraints.
- Design Generative AI, Agentic AI and RAG solution blueprints (LLMs, embeddings, vector DBs, multi-agent workflows).
- Oversee model lifecycle — selection, training/validation, deployment and monitoring (drift, accuracy, latency, cost).
- Establish MLOps/LLMOps standards, observability, and production-readiness controls.
- Drive Responsible AI & governance — security, privacy, RBAC, prompt-injection prevention, compliance and audit readiness.
- Provide architectural reviews and technical guidance across delivery teams; mentor engineers and translate requirements into designs.
- Partner with Product, Data Engineering, Security, Cloud/Platform and business stakeholders on feasibility and trade-offs.
Required Skills
- GenAI/LLMs: Azure OpenAI, OpenAI, Anthropic, Gemini or AWS Bedrock; prompt engineering.
- RAG & Agentic frameworks: LangChain, LangGraph, Semantic Kernel, CrewAI, AutoGen; embeddings & vector DBs (Milvus, OpenSearch, pgvector).
- Programming & architecture: Python, API/microservices, FastAPI, JSON schema/Pydantic.
- Cloud & data: Azure / AWS / GCP, MLOps fundamentals, SQL/NoSQL, data engineering.
- Architecture leadership: reference architectures, scalability/security design, model governance.
Preferred Experience
- Banking & Financial Services (BFSI), Wealth Management / Capital Markets.
- AI-assisted software engineering, knowledge assistants and agentic workflows.
SkillsMandatory Skills :ML OPS
Desirable Skills : Python| Azure | AWS || GCP|| SQL/NO SQL|
Skills To Be Evaluated On : ML OPS