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Salary
≈ $23k – $47k per year (Estimated)
Location
In office (Gurgaon)
Seniority
Senior · 7+ years exp

First seen by Alion on Oct 24, 2026.

Overview
Company
Impact
Profile match
NeoSOFT Technologies is an Indian IT services company that provides custom software development, mobile and web application development and dedicated offshore engineering teams to clients worldwide. Its developers work in Java, .NET, Oracle and full-stack technologies from development centres in Mumbai, Pune, Bengaluru, Ahmedabad and other Indian cities. The company is headquartered in Mumbai, Maharashtra, India.

We are looking for a seasoned Senior Machine Learning Engineer with over 7 years of experience specialising in Large Language Models (LLM) and Retrieval-Augmented Generation (RAG). The role involves developing advanced AI solutions using machine learning techniques and containerised environments.


Responsibilities:

  • Design, develop, and deploy machine learning models to solve complex business problems.
  • Work extensively with cloud platforms such as AWS, Azure, or GCP to build scalable solutions.
  • Utilise SQL to manage data extraction and transformation for analytics and model training.
  • Develop interactive dashboards and reports using Power BI to provide actionable insights.
  • Collaborate with cross-functional teams to integrate ML solutions into business processes.
  • Continuously monitor and optimise model performance.
  • Design, develop, and optimise large language models and retrieval-augmented generation systems.
  • Build and deploy scalable machine learning models using Docker for containerization.
  • Write complex SQL queries for data extraction, manipulation, and preparation for model training.
  • Collaborate with data scientists, engineers, and business teams to integrate AI solutions.
  • Monitor and improve the performance of deployed models to ensure accuracy and efficiency.
  • Stay updated with emerging trends and technologies in LLM, RAG, and machine learning.

Skills

LLM, ML, RAG, large language model, large language models, machine learning, retrieval-augmented generation

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