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.