AI Engineer

Pi Square Technologies; Bengaluru; Hybrid; INR 1,500,000 - 2,100,000; Full time

Job summary

Pi Square Technologies seeks an AI & LLM Developer to design, implement, and optimize agentic AI solutions in production environments. You will build autonomous agents, enable tool-calling, and ensure robust multi-step reasoning capabilities across systems.

You will collaborate with product, data science, and backend teams to deploy production-ready AI workflows, containerize solutions with Docker/Kubernetes, and manage scalable MLOps pipelines on cloud platforms.

Qualifications

• Proficient in Python, SQL, and GenAI frameworks such as LangChain, LangGraph, CrewAI, and AutoGen.

Responsibilities

• Build, evaluate, and optimize AI agents with tool-calling, memory, guardrails, and structured outputs.
• Implement agentic workflows, multi-agent orchestration, and multi-step reasoning patterns (e.g., ReAct).
• Design robust Retrieval-Augmented Generation (RAG) pipelines and context management systems.
• Integrate open-source and commercial foundation models (e.g., LLaMA, Phi, GPT-4, Claude) into production workflows.

Skills

Python

SQL

GenAI frameworks

Agentic design

Tools

LangChain

LangGraph

CrewAI

AutoGen

Job description

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 new AI tools, frameworks, and model architecture updates.

Skills & Qualifications Required

• AI & LLM Stack: Strong 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.