Agentic AI Architect & Developer

Location: Bangalore, Karnataka, India
Positions: 2
Employment type: Full-time
Experience: 7–11 years

Key ResponsibilitiesAgentic 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, including 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, and open-source models), selecting appropriate models and applying 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 with FastAPI to 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 with Celery and event-driven autoscaling with 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 into 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, such as Cursor AI and 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, or AutoGen) in production systems.
- Hands-on experience with LLM API integration (OpenAI, Azure OpenAI, or 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 software development lifecycle.




- Proficiency with Git, CI/CD pipelines, and cloud platforms, preferably Azure.

Preferred Qualifications
- Experience with vector databases (Qdrant, Pinecone, Weaviate, or 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 (e.g., GPT models), Azure OpenAI, Anthropic (Claude), and enterprise LLM services
- Techniques: RAG, prompt engineering, chain-of-thought, function calling, and 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, and agent evaluation

Experience & Qualifications
- Bachelor’s degree in Computer Science, Engineering, AI/ML, or a related technical field, or equivalent professional experience.
- 7+ years of software engineering experience, including 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.
- Robust knowledge of Agile methodologies and principles.
- Demonstrated interest in staying current with the rapidly evolving AI landscape.

📌 Agentic AI Architecture & Development (Karnataka)
🏢 Cephas Consultancy Services Private
📍 Karnataka