Machine Learning Engineer

Sonata Software; Pune District; Hybrid; INR 1,800,000 - 2,800,000; Full time

Job summary

Sonata Software is seeking a Machine Learning Engineer in Pune with 6-8 years of software engineering experience and strong Python development. The role focuses on designing and deploying ML and AI-powered solutions across customer-facing and operational workflows.

You will build scalable data pipelines, apply ML techniques, and collaborate with cross-functional teams to deliver production-ready AI/ML services on cloud platforms like AWS or Azure. The position is hybrid in Pune.

Qualifications

• 6–8 years of software engineering experience with Python.
• 3+ years building and deploying ML solutions.
• Hands-on experience with Agentic AI using LangGraph, LangChain, AutoGen, CrewAI.
• Strong understanding of supervised and unsupervised learning.
• Experience with recommendation systems, forecasting, classification, or anomaly detection.
• Hands-on with ML frameworks: Scikit-Learn, XGBoost, LightGBM, TensorFlow, PyTorch.
• Strong SQL and data analysis skills.
• Experience with feature engineering, model evaluation, and experimentation.
• Familiarity with MLOps, deployment, monitoring, and lifecycle management.

Responsibilities

• Design, develop, deploy ML models for prediction, recommendation, optimization, and forecasting.
• Build scalable ML pipelines from data prep to deployment.
• Apply ML techniques to solve business problems using structured data.
• Collaborate with Product, Engineering, and Business teams to deliver production AI/ML solutions.
• Build and maintain data pipelines from enterprise systems and APIs.
• Develop reusable data services and ML components.
• Implement CI/CD pipelines for ML models and AI services.
• Establish model monitoring, retraining, and deployment processes.
• Follow best practices for software engineering, testing, observability, and documentation.
• Contribute to reusable frameworks and engineering standards across the AI team.

Skills

Python development

ML solution development

Agentic AI

SQL

Scikit-Learn

XGBoost

TensorFlow

PyTorch

MLOps

Docker

CI/CD

Git

AWS or Azure

Tools

LangGraph

LangChain

AutoGen

CrewAI

Job description

Job Summary

Job Title: Machine Learning Engineer

Location: Pune | Hybrid

Experience: 6-8 Years

Primary Skills: Python, SQL, Machine Learning, ML Modelling, Agentic AI

Role Overview

We are building AI-powered solutions that help businesses improve customer outcomes, operational efficiency, revenue growth, and decision-making through the practical application of Machine Learning and AI.

As part of the AI Engineering team, you will work on the design, development, deployment, and optimization of ML-driven solutions that deliver measurable business value across customer-facing and operational workflows.

Roles and Responsibilities

Machine Learning Solution Development

• Design, develop, and deploy Machine Learning models for prediction, recommendation, optimization, classification, and forecasting use cases.
• Build scalable ML pipelines covering data preparation, feature engineering, model training, evaluation, and deployment.
• Apply statistical and machine learning techniques to solve business problems using structured and semi-structured data.
• Collaborate with Product, Engineering, and Business teams to translate requirements into production-ready AI/ML solutions.

Data Engineering & Integration

• Build and maintain data pipelines to ingest, transform, and process data from enterprise systems, APIs, databases, and external sources.
• Develop reusable data services and ML components to accelerate solution delivery.
• Ensure data quality, reliability, and scalability for model development and production workloads.

MLOps & Productionization

• Implement CI/CD pipelines for ML models and AI services.
• Establish model monitoring, performance tracking, retraining, and deployment processes.
• Manage model lifecycle, experimentation, versioning, and governance.
• Support deployment of AI/ML workloads on AWS or Azure cloud platforms.

Engineering Excellence

• Follow best practices for software engineering, testing, observability, and documentation.
• Leverage AI-assisted development tools to improve engineering productivity.
• Contribute to reusable frameworks, engineering standards, and best practices across the AI team.

Qualifications & Required Skills

Mandatory Skills

• 6-8 years of software engineering experience with strong Python development skills.
• 3+ years of hands-on experience building and deploying Machine Learning solutions.
• Experience building Agentic AI solutions using LangGraph, LangChain, AutoGen, CrewAI, or similar frameworks.
• Strong understanding of supervised and unsupervised learning techniques.
• Experience with recommendation systems, predictive analytics, forecasting, classification, anomaly detection, or optimization.
• Hands-on experience with Scikit-Learn, XGBoost, LightGBM, TensorFlow, PyTorch, or equivalent ML frameworks.
• Strong SQL and data analysis skills.
• Experience with feature engineering, model evaluation, and experimentation.
• Familiarity with MLOps, model deployment, monitoring, and lifecycle management.
• Experience building data pipelines and integrating enterprise systems through APIs and databases.
• Experience with Docker, CI/CD, Git, and modern software engineering practices.
• Experience working with AWS or Azure.
• Strong analytical, problem-solving, and communication skills.

Good to Have Skills

Advanced AI & Data

• Optimization techniques, routing algorithms, scheduling, or Operations Research.
• Demand forecasting, customer propensity modeling, pricing analytics, and recommendation engines.
• Explainable AI, model evaluation frameworks, and experimentation methodologies.
• Snowflake, Databricks, or modern cloud data platforms.
• Large-scale data processing and distributed computing.
• Analytical dashboards and decision-support solutions.

Generative AI

• LLMs, RAG architectures, vector databases, and agentic frameworks.
• Integration of ML solutions with GenAI applications.

Domain Knowledge

• Sales, pricing, customer intelligence, e-commerce, distribution, logistics, supply chain, or ERP/CRM ecosystems.

Technology Stack

• Languages: Python, SQL
• ML Frameworks: Scikit-Learn, XGBoost, LightGBM, TensorFlow, PyTorch
• Agentic AI: LangGraph, LangChain, AutoGen, CrewAI
• Data: Snowflake, SQL, APIs, Data Pipelines
• MLOps: MLflow, Docker, CI/CD, Model Monitoring
• Cloud: AWS / Azure
• Development Tools: GitHub, Azure DevOps, GitLab, GitHub Copilot, Cursor