Machine Learning Engineer

NewVision Software; Pune District; On-site; INR 1,400,000 - 2,200,000; Full time

Job summary

NewVision Software in Pune is seeking a skilled Machine Learning Engineer to develop, deploy, and maintain ML and DL solutions in production environments. You will build end-to-end pipelines, optimize models, and collaborate with data teams to deliver business value.

The ideal candidate has 3–6 years of hands-on experience with ML frameworks (TensorFlow, Keras, PyTorch), MLflow, SQL, and Python, and a passion for scalable, robust ML systems.

Qualifications

• 3–6 years hands-on ML/DL development and deployment experience.
• Strong knowledge of ML algorithms, model development, and evaluation.
• Experience with MLflow, SQL, and modern ML development practices.

Responsibilities

• Design, train, and deploy ML/DL models for business use cases.
• Build end-to-end ML pipelines including data collection and preprocessing.
• Develop scalable, production-ready ML solutions.
• Perform model evaluation, hyperparameter tuning, and performance optimization.
• Implement experiment tracking and model versioning with MLflow.
• Collaborate with cross-functional teams to translate requirements into AI solutions.
• Monitor deployed models and improve performance over time.
• Document model architecture and deployment processes.

Skills

ML algorithms

Model development

Data preprocessing

Model evaluation

Deployment

MLflow

SQL

Python

NumPy

Pandas

Scikit-learn

TensorFlow

Keras

PyTorch

Docker

REST API

CI/CD

Git

Spark

LLMs

Evidently AI

Tools

MLflow

SQL

Python

TensorFlow

Keras

PyTorch

Docker

FastAPI/Flask

Git

Spark

Job description

We are looking for a talented and motivated Machine Learning Engineer with 3–6 years of hands‑on experience in developing, deploying, and maintaining Machine Learning and Deep Learning solutions. The ideal candidate should have strong knowledge of ML algorithms, model development, data preprocessing, model evaluation, and production deployment. Experience with MLflow, SQL, and modern ML development practices is essential.

Key Responsibilities

• Design, develop, train, and deploy Machine Learning and Deep Learning models for business use cases.
• Build end-to-end ML pipelines, including data collection, preprocessing, feature engineering, model training, validation, and deployment.
• Develop scalable and production-ready machine learning solutions.
• Perform model evaluation, hyperparameter tuning, and performance optimization.
• Implement experiment tracking, model versioning, and model lifecycle management using MLflow.
• Work with structured and semi-structured datasets.
• Create reusable ML components and automation pipelines.
• Collaborate with cross‑functional teams to understand business requirements and translate them into AI solutions.
• Monitor deployed models and improve model performance over time.
• Document model architecture, assumptions, and deployment processes.
• Stay updated with the latest advancements in Machine Learning, Deep Learning, and Generative AI technologies.

Required Skills

• Strong understanding of supervised and unsupervised learning algorithms.
• Experience with classification, regression, clustering, anomaly detection.
• Feature engineering and feature selection techniques.
• Model evaluation metrics and validation strategies.
• Hyperparameter tuning and optimization.
• Model interpretability and explainability.
• Good understanding of neural networks and deep learning architectures.
• Hands‑on experience with Transformer-based architectures (preferred)
• Experience using TensorFlow, Keras, or PyTorch.

Programming

• Strong proficiency in Python.
• Experience with NumPy, Pandas, Scikit-learn, Matplotlib, and related ML libraries.
• Knowledge of object-oriented programming and software engineering best practices.

SQL

• Experience writing complex queries, joins, aggregations, subqueries, and performance optimization.
• Ability to work with relational databases efficiently.

MLflow

• Hands‑on experience with MLflow.
• Experiment tracking.

Preferred Skills

• Experience with cloud platforms such Azure.
• Knowledge of Docker and containerized ML deployments.
• Experience with REST API development using Flask or FastAPI.
• Familiarity with Git and CI/CD pipelines.
• Exposure to MLOps concepts and deployment best practices.
• Understanding of distributed data processing using Spark is a plus.
• Exposure to Large Language Models (LLMs), Generative AI is an added advantage.
• Hands‑on experience with Evidently AI for model monitoring, data quality validation, drift detection, and performance reporting.
• Experience using automated code quality and code review tools such as SonarQube, CodeClimate, or similar platforms to ensure code quality, maintainability, and adherence to best practices.