Role & responsibilities

Agentic AI & LLM Development • Build, evaluate, and optimize AI agents using frameworks such as LangChain, LangGraph, CrewAI, or AutoGen.
Implement agentic workflows, multi-agent orchestration, tool integration, state management, and multi-step reasoning patterns (e.g., ReAct).
Design robust Retrieval-Augmented Generation (RAG) pipelines, prompt tuning strategies, and context management systems.
Integrate, fine-tune, or adapt both open-source (e.g., LLaMA, Phi) and commercial foundation models (e.g., GPT-4, Claude).Deployment & MLOps • Package AI models and agentic workflows into production-ready REST APIs.
Containerize and deploy solutions using Docker and Kubernetes across cloud platforms (AWS, Azure, or GCP).
Establish MLOps pipelines covering model tracking, automated deployment (CI/CD), observability, and vector database management.




Optimize system latency, token usage, and infrastructure cost for production workloads. Cross-Functional Collaboration • Work alongside product managers, data scientists, and backend engineers to integrate AI capabilities into application ecosystems.
Conduct experiments to evaluate recent AI tools, frameworks, and model architecture updates.

Skills &

Qualifications Required • AI & LLM Stack: Robust proficiency in Python, SQL, and GenAI frameworks (LangChain, LangGraph, CrewAI, AutoGen).
Agentic Design: Hands-on experience building agents with tool-calling capabilities, memory retention, guardrails, and structured outputs.

📌 Ai Engineer Bengaluru
🏢 Pi Square Technologies
📍 Bengaluru