AI/ML Engineer

Job Title: AI/ML Engineer

Location: Pune | Hybrid

Experience: 6-8 Years

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

We are looking for an experienced AI/ML Engineer to join our AI Engineering team and build intelligent, scalable, and production-ready AI/ML solutions.

The role will focus on designing, developing, deploying, and optimizing Machine Learning models and Agentic AI solutions that deliver measurable business value across customer-facing and operational workflows.

The ideal candidate should have strong hands-on experience in Python, Machine Learning, SQL, MLOps, and Agentic AI frameworks, along with the ability to translate business requirements into scalable AI solutions.

• 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.
• Work closely with product, engineering, and business teams to translate requirements into production-ready AI/ML solutions.

• Design and develop 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.
• Combine Agentic AI capabilities with predictive and analytical ML models.

• 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.

• 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 cloud platforms.

• 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.

• 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 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 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 enterprise systems through APIs and databases.
• Experience with Docker, CI/CD, Git, and modern software engineering practices.
• Experience working with AWS or Azure cloud platforms.
• Strong analytical, problem-solving, and communication skills.

• Experience with optimization techniques, routing algorithms, scheduling, or Operations Research.
• Knowledge of demand forecasting, customer propensity modelling, pricing analytics, and recommendation engines.
• Experience with explainable AI, model evaluation frameworks, and experimentation methodologies.

• 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.

• Exposure to LLMs, RAG architectures, vector databases, and Agentic AI frameworks.
• Experience integrating ML solutions with Generative AI applications.

Exposure to Sales, Pricing, Customer Intelligence, E-commerce, Distribution, Logistics, Supply Chain, or ERP/CRM ecosystems is preferred.

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