Generative AI & Data Engineer - Python/LLM

Job Title : Generative AI & Data Engineer

Experience : 8-12 Years

Location : Bangalore

Core Skills Required :

• Generative AI (Large Language Models, Retrieval-Augmented Generation, AI Agents)
• AI model evaluation and Responsible AI practices
• Data Engineering (SQL, ETL, data processing)
• Production Machine Learning and MLOps

Role Overview :

We are seeking an experienced Generative AI & Data Engineer to design, develop, and deploy enterprise-grade AI solutions. The ideal candidate should have strong expertise in building scalable GenAI applications, engineering reliable data pipelines, and deploying machine learning solutions in cloud environments.

Required Qualifications :

• Minimum 6 years of professional experience in software engineering, data engineering, or machine learning.
• Strong ability to gather, analyze, and interpret business requirements.
• Experience translating functional requirements into scalable technical architectures and solution designs.
• Solid understanding of statistical methods, data analysis, and analytical problem-solving.
• Advanced programming skills in Python with hands-on experience building production-ready ML applications.
• Practical experience with Generative AI technologies, including Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and agent-based AI systems.
• Strong background in data engineering, including SQL, ETL processes, and data pipeline development.
• Experience working with at least one major cloud platform (AWS, Azure, or GCP).
• Hands-on expertise with MLOps tools and practices such as Docker, Kubernetes, MLflow, and CI/CD pipelines.
• Knowledge of AI model evaluation, monitoring, governance, and Responsible AI principles.

Key Responsibilities :

• Design, develop, and deploy scalable Generative AI applications using LLMs, RAG, and AI agents.
• Build, optimize, and maintain production-grade machine learning pipelines and Python-based services.
• Develop and manage efficient data ingestion, transformation, and processing workflows.
• Collaborate with business stakeholders, data engineers, infrastructure teams, and clients to deliver AI-driven solutions.
• Deploy and monitor ML models in cloud environments using MLOps best practices.
• Evaluate model performance, improve reliability, and implement Responsible AI standards across AI solutions.
• Ensure AI applications are scalable, secure, and aligned with enterprise architecture and business objectives.