Position: Agentic AI Architecture & Development

Experience Level:6+ Years

Location- Bangalore (Hybrid Role)

Employment: Fulltime

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

Technical Skills & Competencies:

- Primary Framework: LangGraph (multi-agent orchestration with persistent state)
- Additional Frameworks: LangChain, CrewAI, AutoGen
- LLM Providers: OpenAI (GPT-5.X), Azure OpenAI, Anthropic (Claude), enterprise LLM services
- Techniques: RAG, prompt engineering, chain-of-thought, function calling, structured outputs
- Data Intelligence: Databricks Genie (natural language to SQL)
- Vector Databases: Qdrant, Pinecone, Weaviate, ChromaDB
- Multi-Cloud: AWS Bedrock AgentCore (managed agent runtime)
- Patterns: Multi-agent orchestration, tool use, persistent state, memory management, agent evaluation

Experience & Qualifications:

- Bachelor's degree in Computer Science, Engineering, AI/ML, or a related technical field, or equivalent qualified experience
- 6+ years of proven software engineering experience with significant hands-on AI/ML work in enterprise environments
- Strong communication skills with the ability to explain complex AI concepts to technical and non-technical stakeholders
- Strong knowledge of Agile methodologies and principles
- Demonstrated passion for staying current with the rapidly evolving AI landscape

Share resume to:

Vishal Kumar

[email protected]

📌 Agentic AI Architecture & Development (Bengaluru)
🏢 Akaasa Infotech Noida
📍 Bengaluru