Ai Ml Engineer

Navneet Education; Ahmedabad District; On-site; INR 900,000 - 1,600,000; Full time

Job summary

Navneet Education seeks an AI feature developer to build and test LLM-based capabilities, integrating agents with ERP/CRM systems and internal REST APIs. You will design prompts, implement tool calls, and orchestrate multi-agent workflows to automate complex business tasks.

The role requires experience with agent frameworks, embeddings, and Python REST services, plus familiarity with RAG and vector databases. Proven project work with LangGraph, CrewAI, or AutoGen is preferred.

Qualifications

• Projects built with agent frameworks LangGraph, CrewAI, AutoGen, Semantic Kernel, or custom agent loops.

Responsibilities

• Build and test LLM-based features using APIs (Claude, OpenAI, Gemini) prompt design, structured output, and evaluation of response quality.
• Design and develop AI agents that plan, call tools, and complete multi-step business tasks with minimal human intervention.
• Implement tool/function calling and integrate agents with internal REST APIs, databases, and enterprise systems (SAP, CRM, WMS) via MCP or equivalent connector patterns.
• Build multi-agent and workflow-orchestration solutions using frameworks such as LangGraph, CrewAI, AutoGen, or the Claude/OpenAI Agents SDK including memory, state management, and error recovery.
• Develop and maintain conversational flows for WhatsApp/web chatbots, including session handling and backend integration.
• Work on Retrieval-Augmented Generation (RAG) use cases document ingestion, chunking, embeddings, and vector search over internal content.
• Assist in developing natural-language-to-SQL and analytics assistants over MS SQL/enterprise databases with read-only guardrails.
• Write Python scripts and REST APIs (Flask/ FastAPI) to expose AI features to internal applications.
• Define guardrails, approval checkpoints, logging, and evaluation methods so agents behave safely and predictably in production.
• Support data preparation cleaning, labelling, and structuring datasets for model evaluation.
• Document solutions, prepare test cases, and present findings to business stakeholders.

Skills

LLM integration

API design

Prompt design

Agent frameworks

LangGraph

CrewAI

AutoGen

Semantic Kernel

Memory management

REST APIs

Flask/FastAPI

RAG embeddings

Vector databases

Tools

SAP

CRM

WMS

MS SQL

FAISS

Pinecone

Chroma

pgvector

Flask

FastAPI

Streamlit

Node.js

Job description

Role & responsibilities

1. Build and test LLM-based features using APIs (Claude, OpenAI, Gemini) prompt design, structured output, and evaluation of response quality.
2. Design and develop AI agents that plan, call tools, and complete multi-step business tasks with minimal human intervention.
3. Implement tool/function calling and integrate agents with internal REST APIs, databases, and enterprise systems (SAP, CRM, WMS) via MCP or equivalent connector patterns.
4. Build multi-agent and workflow-orchestration solutions using frameworks such as LangGraph, CrewAI, AutoGen, or the Claude/OpenAI Agents SDK including memory, state management, and error recovery.
5. Develop and maintain conversational flows for WhatsApp/web chatbots, including session handling and backend integration.
6. Work on Retrieval-Augmented Generation (RAG) use cases document ingestion, chunking, embeddings, and vector search over internal content.
7. Assist in developing natural-language-to-SQL and analytics assistants over MS SQL/enterprise databases with read-only guardrails.
8. Write Python scripts and REST APIs (Flask/ FastAPI) to expose AI features to internal applications.
9. Define guardrails, approval checkpoints, logging, and evaluation methods so agents behave safely and predictably in production.
10. Support data preparation cleaning, labelling, and structuring datasets for model evaluation.
11. Document solutions, prepare test cases, and present findings to business stakeholders.

Preferred candidate profile

1. Projects built with agent frameworks LangGraph, CrewAI, AutoGen, Semantic Kernel, or custom agent loops.
2. Awareness of the Model Context Protocol (MCP) and how agents connect to external tools and data sources.
3. Exposure to agent evaluation and observability tools (LangSmith, LangFuse, or custom tracing).
4. Academic or personal projects using RAG or vector databases (FAISS, Pinecone, Chroma, pgvector).
5. Exposure to Flask/FastAPI, Streamlit, or Node.js.
6. Basic understanding of ML concepts embeddings, classification, evaluation metrics. Published GitHub repositories, Kaggle work, or hackathon participation