Sonata Software - AI/ML Engineer

AI/ML Engineer. Location : Pune | Hybrid. Experience : 6 - 8 years. Primary Skill : Python, SQL, ML Modelling, Agentic AI. 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. Agentic AI Development : - Design and build agentic workflows using frameworks such as LangGraph, LangChain, AutoGen, CrewAI, or similar. - Develop AI agents capable of reasoning, task orchestration, tool usage, and multi-step workflow execution. - Integrate AI agents with enterprise systems, APIs, databases, and business applications. - Collaborate with AI engineers to combine Agentic AI capabilities with predictive and analytical ML models. 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 - 8 years of software engineering experience with strong Python development skills. - 3+ years of hands-on experience building and deploying Machine Learning solutions. - Hands-on experience building Agentic AI solutions using frameworks such as LangGraph, LangChain, AutoGen, CrewAI, or similar. - Strong 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 modern 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. - Agentic AI : LangGraph, LangChain, AutoGen, CrewAI. - 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.