Data Engineer ( AI/ML )

Design, develop, and maintain scalable data pipelines and cloud-based data platforms supporting AI and ML workloads.
• Build, deploy, and optimize machine learning models and AI solutions for enterprise-scale applications.
• Develop robust data processing and feature engineering solutions using Python, SQL, and PySpark.
• Support end-to-end ML lifecycle management, including model deployment, monitoring, automation, and governance.
• Implement and maintain CI/CD pipelines for data and machine learning applications.
• Collaborate with data scientists, engineers, and business stakeholders to operationalize AI/ML solutions.
• Ensure data quality, scalability, reliability, and performance across data and ML platforms.

All About You -
• 5–6 years of experience in Data Engineering, Machine Learning Engineering, or AI-related roles.
• Strong programming expertise in Python.
• Expert-level SQL skills, including data modeling, query optimization, performance tuning, and complex data transformations.
• Strong hands-on experience with PySpark and distributed data processing.
• Experience developing, deploying, and supporting machine learning models in production environments.
• Solid understanding of AI/ML concepts, including Generative AI, LLMs, RAG architectures, AI agents, prompt engineering, and model lifecycle management.
• Experience building and maintaining scalable ETL/ELT pipelines, data lakes, and cloud-based data platforms.
• Hands-on experience with cloud platforms such as AWS, Azure, or GCP.
• Strong understanding of MLOps, including model deployment, monitoring, versioning, automation, and governance.
• Experience implementing CI/CD pipelines and DevOps best practices.
• Knowledge of containerization, orchestration, and cloud-native architectures.
• Strong analytical, problem-solving, and communication skills.
• Experience working in Agile/Scrum environments.
• Ability to collaborate effectively with data engineers, software engineers, data scientists, and business stakeholders.
• Experience integrating AI/ML capabilities into enterprise data platforms and business applications.
• Understanding of data governance, model governance, security, and responsible AI practices.