Machine Learning Engineer

Nexkites; Pune District; On-site; INR 1,200,000 - 1,800,000; Full time

Job summary

Nexkites is seeking an experienced ML Engineer to design, train, and fine-tune models for computer vision, document parsing, and predictive analytics. You will build robust ETL pipelines, deploy real-time inference services, and monitor performance in production.

Join a team that collaborates with backend engineers to embed intelligent agents in client systems, leveraging PyTorch/TensorFlow and modern MLOps tools to deliver scalable solutions.

Qualifications

• 3+ years of professional ML model development and deployment.
• Experience in computer vision, NLP, or analytics.
• Strong foundation in linear algebra and statistics.

Responsibilities

• Design, train, fine-tune, and evaluate ML models for computer vision, document parsing, and predictive analytics.
• Build robust ETL and feature engineering pipelines that handle heterogeneous data sources.
• Deploy inference endpoints with sub-second latencies and automated regression testing.
• Monitor model drift, latency, and data accuracy in production environments.
• Partner with backend engineers to integrate intelligent agents into client systems.
• 3+ years of professional experience building and deploying ML models in production.

Skills

Python

PyTorch

TensorFlow

Scikit-learn

Vector databases

Embeddings

RAG pipelines

ML deployment

ETL

Model monitoring

Tools

MLflow

Kubeflow

BentoML

Job description

Pune, India · 40 hours a week · Role number NX-AI-003

About the role

The Nexkites AI practice deploys bespoke machine learning intelligence into operational workflows. In this role, you will develop machine learning pipelines, fine-tune models, and deploy real-time inference services that solve non-trivial business challenges.

What you will do

• Design, train, fine-tune, and evaluate ML models for computer vision, document parsing, and predictive analytics.
• Build robust ETL and feature engineering pipelines that handle heterogeneous business data sources.
• Deploy inference endpoints with sub-second latencies and automated regression testing.
• Monitor model drift, latency, and data accuracy in production environments.
• Partner with backend engineers to integrate intelligent agents into existing client systems.
• 3+ years of professional experience building and deploying machine learning models in production.
• Proficiency in Python and deep learning frameworks (PyTorch, TensorFlow, Scikit-learn).
• Experience with vector databases, embeddings, and modern retrieval-augmented generation (RAG) pipelines.
• Strong background in linear algebra, statistics, and algorithm design.

Preferred qualifications

• Experience with LLM fine-tuning techniques (LoRA, QLoRA) and agentic frameworks.
• Published research or tangible real-world deployments in intelligent automation.
• Experience with MLOps platforms (MLflow, Kubeflow, BentoML).