Posted 17 June, 2026
Agentic AI Architecture & Development
NR Consulting - India
Bangalore/ Chennai/ Hyderabad, IN
Full Time
Reference: 26-06554-2220-1
Title: Agentic AI Architecture & Development
Location: Bangalore/ Chennai/ Hyderabad
Exp: 6+ Yrs
Job Description:
Key Responsibilities:
Agentic AI Architecture & Development:
• Design and build production-grade multi-agent systems using LangGraph as the primary orchestration framework, with knowledge of LangChain, CrewAI, and AutoGen
• Architect agent orchestration patterns including planning, tool use, persistent state management, memory, reflection, and multi-agent coordination
• Develop and optimize RAG (Retrieval-Augmented Generation) pipelines with document processing, chunking strategies, embedding workflows, and vector database integration
• Build robust agent evaluation, testing, and observability frameworks to ensure reliability and performance in production
• Design natural language to data query solutions integrating with platforms such as Databricks Genie
LLM Integration & Optimization
• Integrate and manage LLM/SLM services (OpenAI, Azure OpenAI, Anthropic, open-source models) with appropriate model selection, prompt engineering, and cost optimization
• Design prompt engineering strategies including chain-of-thought, few-shot, and structured output techniques for reliable agent behavior
• Implement guardrails, safety mechanisms, and content filtering for AI-generated outputs
• Evaluate and benchmark models for latency, accuracy, cost, and domain-specific performance
Platform & Backend Engineering:
• Build scalable Python backend services (FastAPI) that serve AI agent workflows to production applications at enterprise scale
• Design and implement caching, rate limiting, persistent agent state, and conversation memory strategies
• Develop event-driven microservices and real-time streaming for AI agent interactions
• Develop APIs and integration layers that connect AI agents with enterprise data sources, tools, and external services
• Implement distributed task processing (Celery) and event-driven autoscaling (KEDA) for production AI workloads
Innovation & Technical Leadership:
• Stay current with the rapidly evolving Agentic AI landscape and evaluate emerging frameworks, models, and techniques
• Lead proof-of-concept development for new AI capabilities, moving successful experiments to production
• Mentor engineers on AI engineering best practices, prompt engineering, and agent design patterns
• Contribute to technical documentation, architecture decision records, and AI solution design specifications
• Champion the adoption of AI-powered development tools (Cursor AI, GitHub Copilot) across engineering teams
Required Qualifications:
• Strong proficiency in Python with hands-on experience building production AI applications
• Demonstrated experience with LangGraph or similar agentic AI frameworks (LangChain, CrewAI, AutoGen) for production systems
• Hands-on experience with LLM API integration (OpenAI, Azure OpenAI, Anthropic) and prompt engineering
• Experience designing and implementing RAG systems including embedding models, vector databases, and retrieval strategies
• Solid understanding of multi-agent system design, agent orchestration, persistent state management, and memory patterns
• Experience with Python web frameworks (FastAPI) and distributed task processing (Celery) for production APIs
• Experience with event-driven microservices and real-time streaming patterns
• Proficiency with AI-powered development tools (Cursor AI, GitHub Copilot, or similar) for AI-augmented software development across the SDLC
• Proficiency with Git, CI/CD pipelines, and cloud platforms (preferably Azure)
Preferred Qualifications:
• Experience with vector databases (Qdrant, Pinecone, Weaviate, ChromaDB)
• Experience with Databricks Genie or similar natural language to data query platforms
• Experience with AWS Bedrock AgentCore for managed agent runtime and multi-cloud agent deployment
• Knowledge of model fine-tuning, quantization, and serving optimization
• Experience with multi-tenant architecture patterns and enterprise-scale AI systems
• Experience with containerization (Docker, Kubernetes) and event-driven autoscaling (KEDA)
• Understanding of AI safety, responsible AI principles, and enterprise governance requirements
Location: Bangalore/ Chennai/ Hyderabad
Exp: 6+ Yrs
Job Description:
Key Responsibilities:
Agentic AI Architecture & Development:
• Design and build production-grade multi-agent systems using LangGraph as the primary orchestration framework, with knowledge of LangChain, CrewAI, and AutoGen
• Architect agent orchestration patterns including planning, tool use, persistent state management, memory, reflection, and multi-agent coordination
• Develop and optimize RAG (Retrieval-Augmented Generation) pipelines with document processing, chunking strategies, embedding workflows, and vector database integration
• Build robust agent evaluation, testing, and observability frameworks to ensure reliability and performance in production
• Design natural language to data query solutions integrating with platforms such as Databricks Genie
LLM Integration & Optimization
• Integrate and manage LLM/SLM services (OpenAI, Azure OpenAI, Anthropic, open-source models) with appropriate model selection, prompt engineering, and cost optimization
• Design prompt engineering strategies including chain-of-thought, few-shot, and structured output techniques for reliable agent behavior
• Implement guardrails, safety mechanisms, and content filtering for AI-generated outputs
• Evaluate and benchmark models for latency, accuracy, cost, and domain-specific performance
Platform & Backend Engineering:
• Build scalable Python backend services (FastAPI) that serve AI agent workflows to production applications at enterprise scale
• Design and implement caching, rate limiting, persistent agent state, and conversation memory strategies
• Develop event-driven microservices and real-time streaming for AI agent interactions
• Develop APIs and integration layers that connect AI agents with enterprise data sources, tools, and external services
• Implement distributed task processing (Celery) and event-driven autoscaling (KEDA) for production AI workloads
Innovation & Technical Leadership:
• Stay current with the rapidly evolving Agentic AI landscape and evaluate emerging frameworks, models, and techniques
• Lead proof-of-concept development for new AI capabilities, moving successful experiments to production
• Mentor engineers on AI engineering best practices, prompt engineering, and agent design patterns
• Contribute to technical documentation, architecture decision records, and AI solution design specifications
• Champion the adoption of AI-powered development tools (Cursor AI, GitHub Copilot) across engineering teams
Required Qualifications:
• Strong proficiency in Python with hands-on experience building production AI applications
• Demonstrated experience with LangGraph or similar agentic AI frameworks (LangChain, CrewAI, AutoGen) for production systems
• Hands-on experience with LLM API integration (OpenAI, Azure OpenAI, Anthropic) and prompt engineering
• Experience designing and implementing RAG systems including embedding models, vector databases, and retrieval strategies
• Solid understanding of multi-agent system design, agent orchestration, persistent state management, and memory patterns
• Experience with Python web frameworks (FastAPI) and distributed task processing (Celery) for production APIs
• Experience with event-driven microservices and real-time streaming patterns
• Proficiency with AI-powered development tools (Cursor AI, GitHub Copilot, or similar) for AI-augmented software development across the SDLC
• Proficiency with Git, CI/CD pipelines, and cloud platforms (preferably Azure)
Preferred Qualifications:
• Experience with vector databases (Qdrant, Pinecone, Weaviate, ChromaDB)
• Experience with Databricks Genie or similar natural language to data query platforms
• Experience with AWS Bedrock AgentCore for managed agent runtime and multi-cloud agent deployment
• Knowledge of model fine-tuning, quantization, and serving optimization
• Experience with multi-tenant architecture patterns and enterprise-scale AI systems
• Experience with containerization (Docker, Kubernetes) and event-driven autoscaling (KEDA)
• Understanding of AI safety, responsible AI principles, and enterprise governance requirements
