ML Engineer

Location:
Pune | Hybrid

Experience:
8+ years Primary Skill: Python, SQL, ML Modelling, ML Operations, Data science Our Objective 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.

Key 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 for 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.
- Work closely 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 and ML workloads on 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, standards, and best practices across the AI team. Required Qualifications
- 6+ years of software engineering experience with strong Python development skills.
- 5+ years of hands-on experience building and deploying Machine Learning solutions.
- Robust understanding of supervised and unsupervised learning techniques.
- Experience with recommendation systems, predictive analytics, forecasting, classification, anomaly detection, or optimization problems.
- 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 frameworks.
- Familiarity with MLOps practices, model deployment, monitoring, and lifecycle management.
- Experience building data pipelines and integrating with enterprise systems through APIs and databases.
- Experience with Docker, CI/CD pipelines, Git,



and modern software engineering practices.
- Experience working with AWS or Azure cloud platforms.
- Strong analytical, problem-solving, and communication skills. Good to Have *Advanced AI & Data Platforms*
- Experience with optimization techniques, routing algorithms, scheduling, or Operations Research.
- Knowledge of demand forecasting, customer propensity modeling, pricing analytics, and recommendation engines.
- Experience with explainable AI, model evaluation frameworks, and experimentation methodologies. *Data & Analytics*
- Knowledge of Operations Research, routing algorithms, scheduling, or decision optimization techniques.
- Experience with demand forecasting, pricing analytics, customer intelligence, propensity modeling, and recommendation engines.
- Experience with Snowflake, Databricks, or up-to-date cloud data platforms.
- Experience building analytical dashboards and decision-support solutions.
- Familiarity with large-scale data processing and distributed computing. *Generative AI*
- Exposure to LLMs, RAG architectures, vector databases, and agentic frameworks.
- Experience integrating ML solutions with GenAI applications. *Domain Knowledge*
- Exposure to 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
- Data: Snowflake, SQL, APIs, Data Pipelines
- MLOps: MLflow, Docker, CI/CD, Model Monitoring
- Cloud: AWS or Azure
- Development Tools: GitHub, Azure DevOps, GitLab, GitHub Copilot, Cursor

📌 Machine Learning Specialist (Pune)
🏢 Sonata Software
📍 Pune