AI / ML Engineer

Netopsys Pte. Ltd.; India; On-site; INR 1,400,000 - 2,200,000; Full time

Job summary

Netopsys AI Pvt Ltd in India seeks a hands-on AI Engineer to build production-ready Generative AI apps using Python, LLMs and agentic workflows. You will design RAG pipelines, implement tool calling and develop REST APIs with FastAPI or Flask, integrating OpenAI, Hugging Face and local models.

You'll work with embeddings and vector databases (pgvector, FAISS, ChromaDB), Speech-to-Text and Voice AI, and deploy AI services.

Qualifications

• Bachelor's degree or equivalent practical experience in a technical field.
• 1–3 years of hands-on software/AI engineering experience.
• Strong programming skills in Python and experience with LLMs.

Responsibilities

• Design and develop production-ready Generative AI apps using Python and LLMs.
• Build RAG pipelines for document understanding and knowledge retrieval.
• Develop Agentic AI workflows with tool and function calling.

Skills

Python

LLMs

REST APIs

RAG

Docker

Databases

AI Orchestration

Embeddings

Speech/Voice AI

Education

Bachelor's degree in CS/AI/IT or equivalent

Tools

FastAPI

Flask

Git

Docker

OpenAI API

Hugging Face

pgvector

FAISS

ChromaDB

Job description

AI Engineer – Generative AI & Agentic Systems

AI Engineer – Generative AI & Agentic Systems

We are looking for a hands-on Generative AI Engineer to build production-ready AI applications using LLMs, RAG, Agentic AI, Python and modern AI technologies.

Description

• Design and develop production-ready Generative AI applications using Python, LLMs and modern AI technologies.


• Build Retrieval-Augmented Generation (RAG) pipelines for document understanding, enterprise knowledge retrieval and context-aware responses.


• Develop Agentic AI workflows using tool calling, function calling, structured outputs and multi-step reasoning.


• Build and maintain backend AI services and REST APIs using Python and frameworks such as FastAPI or Flask.


• Integrate LLM platforms including OpenAI, Hugging Face and other hosted or local AI models.


• Work with embeddings and vector databases such as pgvector, FAISS, ChromaDB or similar technologies.


• Develop conversational AI and Voice AI solutions using Speech-to-Text, Text-to-Speech and real-time audio technologies.


• Integrate AI capabilities with web, mobile and enterprise applications.


• Design AI workflows capable of interpreting user commands and securely interacting with application functions and external tools.


• Implement prompt engineering, context management, structured prompting and model-response validation.


• Improve AI response quality, retrieval accuracy, latency, reliability and overall application performance.


• Evaluate model outputs and implement techniques to reduce hallucinations and improve answer relevance.


• Implement chunking, metadata filtering, retrieval, reranking and other RAG optimisation techniques.


• Work with relational, NoSQL and vector databases as required by the application.


• Containerize and deploy AI services using Docker and modern deployment environments.


• Debug issues across AI models, APIs, databases, integrations and production services.


• Participate in architecture discussions, code reviews, testing and technical documentation.


• Research and evaluate emerging LLM, Agentic AI, Voice AI and Generative AI technologies.


• Collaborate with frontend, backend, QA and product teams to deliver complete AI-powered products.

Requirements

• Bachelor's degree in Computer Science, Artificial Intelligence, Machine Learning, Information Technology or a related technical field, or equivalent practical experience.


• 1–3 years of hands-on experience in software engineering, AI engineering, Generative AI or a related field.


• Strong programming skills in Python.


• Practical experience working with LLMs and Generative AI applications.


• Hands-on experience integrating AI models or third-party AI APIs into applications.


• Experience developing REST APIs and backend services.


• Good understanding of Machine Learning, NLP and Generative AI fundamentals.


• Understanding of prompt engineering, context management and structured LLM outputs.


• Experience or strong practical knowledge of RAG architecture.


• Understanding of embeddings, semantic search and vector databases.


• Experience with FastAPI, Flask or similar Python backend frameworks.


• Understanding of AI Agents, tool calling, function calling and multi-step AI workflows.


• Working knowledge of databases such as PostgreSQL, MongoDB, MySQL or similar technologies.


• Experience with Git and collaborative software development workflows.


• Strong debugging, analytical and problem-solving skills.


• Ability to explain projects personally designed or developed, including architecture, implementation decisions and individual contribution.


• Experience with LangChain, LangGraph, LlamaIndex or similar AI orchestration frameworks is an advantage.


• Experience with pgvector, FAISS, ChromaDB or other vector databases is an advantage.


• Experience with Speech-to-Text, Text-to-Speech, Whisper or Voice AI technologies is an advantage.


• Familiarity with MCP or similar AI tool-integration architectures is an advantage.


• Experience with Docker, CI/CD, cloud platforms and production deployment is an advantage.