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.

- - - - Positive 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

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