AI / ML Engineer

Machine Learning Solution Development

• 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.

Agentic AI Development

• 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.

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/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.