Machine Learning Engineer

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

Job summary

NewVision Software is seeking a talented Machine Learning Engineer with 3–6 years of hands-on experience to design, develop, and deploy ML solutions. The role focuses on scalable pipelines, model evaluation, and production deployment using MLflow, SQL, and modern ML practices.

The ideal candidate will work with structured data, implement experiment tracking, and collaborate with cross-functional teams to translate business needs into AI applications.

Qualifications

• Hands-on experience in developing, deploying, and maintaining ML/DL solutions.
• Strong knowledge of ML algorithms, model development, data preprocessing, 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: data collection, preprocessing, feature engineering, training, validation, deployment.
• Develop scalable, production-ready ML solutions and monitor performance.

Skills

MLflow

SQL

Python

TensorFlow

PyTorch

Keras

Model deployment

Data preprocessing

Experiment tracking

Docker

REST API

Tools

MLflow

Docker

Flask

FastAPI

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.