Description

You will design, develop, and deploy Generative AI solutions and Machine Learning models for enterprise use cases. Your work spans the full lifecycle from data engineering and model training to production deployment and MLOps.

Responsibilities

• Build, train, evaluate, and deploy ML models using Python frameworks like TensorFlow, PyTorch, and Scikit-learn.
• Design and implement RAG pipelines and fine-tune Large Language Models (LLMs) for specific business needs.
• Develop and maintain ETL pipelines using Python and SQL, leveraging Hive for large-scale data processing.
• Deploy AI/ML models via APIs and microservices, ensuring scalability and performance in production.
• Optimize SQL queries and data schemas to support efficient data extraction and model inference.

Required Skills

• 8+ years of experience in Software Engineering, with a focus on Machine Learning and Generative AI.
• Advanced proficiency in Python for data engineering, ML workflows, and backend services.
• Strong expertise in SQL for complex queries, database optimization, and schema design.
• Hands-on experience with ETL processes and distributed data tools, specifically Apache Hive.
• Proven track record of deploying ML models into production environments using microservices architectures.
• Experience with model monitoring, logging, and CI/CD pipeline integration for AI applications.
• Understanding of prompt engineering strategies and Retrieval-Augmented Generation (RAG) techniques.

Preferred Skills

• Experience with cloud environments and MLOps best practices.
• Knowledge of responsible AI practices, including bias detection and data governance.

Key Skills

Education

Any Graduate