Key Responsibilities

AI Solution Architecture

- Design end-to-end Agentic AI architectures for enterprise use cases.
- Define AI architecture standards, governance frameworks, and implementation blueprints.
- Lead AI transformation and modernization initiatives.
- Establish scalable and secure AI platform architecture patterns.

Agentic AI & Multi-Agent Systems

- Design autonomous AI agents with:
- Reasoning
- Planning
- Memory management
- Task execution
- Decision-making

- Define multi-agent orchestration patterns and collaboration frameworks.
- Implement human-in-the-loop (HITL) validation mechanisms.
- Design agent communication protocols and tool integrations.

Generative AI & LLM Engineering

- Evaluate and architect solutions using:
- GPT
- Claude
- Gemini
- Llama
- Mistral
- Open-source foundation models

- Design prompt engineering and prompt orchestration frameworks.
- Architect enterprise RAG solutions.
- Define semantic search and vector retrieval strategies.

AI Platform & Cloud Architecture

- Architect solutions on:
- Azure OpenAI
- Azure AI Services
- AWS Bedrock
- Amazon SageMaker
- Google Vertex AI

- Integrate AI systems with enterprise applications and APIs.
- Design event-driven and cloud-native AI platforms.

Data & Knowledge Architecture

- Architect enterprise knowledge systems.
- Design vector database solutions using:
- Pinecone
- Chroma
- Weaviate
- Milvus
- Azure AI Search

- Define:

- Data ingestion
- Embeddings
- Chunking
- Indexing
- Retrieval strategies

AI Governance & Security

- Establish responsible AI governance frameworks.
- Implement AI security controls and model guardrails.
- Manage:
- Prompt injection protection




- Hallucination controls
- Data privacy compliance
- Model governance

LLMOps & AgentOps

- Define deployment and operational frameworks for AI systems.
- Implement monitoring and observability strategies.
- Establish evaluation metrics and AI performance KPIs.
- Drive continuous improvement and feedback loops.

Must-Have Skills

Agentic AI & Generative AI

- Strong expertise in:
- Agentic AI
- Multi-Agent Systems
- Generative AI
- LLM Architectures
- RAG Frameworks

AI Frameworks

- Hands-on experience with:
- LangChain
- LangGraph
- Semantic Kernel
- AutoGen
- CrewAI
- OpenAI APIs

Cloud AI Platforms

- Experience with:
- Azure OpenAI
- Azure AI Foundry
- AWS Bedrock
- Amazon SageMaker
- Google Vertex AI

Architecture & Engineering

- Expertise in:
- Solution Architecture
- Microservices
- REST APIs
- Event-Driven Architecture

Programming

- Strong proficiency in:
- Python

AI Data Platforms

- Experience with:
- Vector Databases
- Knowledge Graphs
- Semantic Search
- Embedding Models
- Data Governance

MLOps / LLMOps

- Experience with:
- Model Deployment
- LLMOps
- AgentOps
- Kubernetes
- Docker
- CI/CD

AI Governance & Security

- Strong understanding of:
- Responsible AI
- Model Governance
- AI Security
- Compliance & Privacy Controls

Positive-to-Have Skills

Certifications

- Azure AI Engineer Associate
- Azure Solutions Architect Expert
- AWS Machine Learning Specialty
- Google Professional ML Engineer
- TOGAF
- Certified Kubernetes Administrator (CKA)
- Databricks Generative AI Certification

Domain Experience

- Enterprise digital transformation
- Financial Services
- Banking
- Insurance
- Large-scale AI modernization programs

📌 Agentic AI Solution Architect - PAN India (Hyderabad)
🏢 Tata Consultancy Services
📍 Hyderabad