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.