Sr Data Engineer-AIML and Cloud

Key Responsibilities:•

Design, develop, and maintain ETL/ELT pipelines for large-scale structured and unstructured data.• Build and optimize data models, data lakes, and warehouses using modern cloud platforms (AWS/Azure/GCP).• Collaborate with data scientists to operationalize ML models and automate model training and deployment (MLOps).• Implement data quality, validation, and monitoring frameworks to ensure reliable pipelines.• Develop feature stores and real-time data streaming solutions for AI/ML use cases.• Work with business and product teams to understand data requirements and translate them into scalable engineering solutions.• Use AI/ML techniques to enhance data enrichment, predictive analytics, and process automation.• Manage and optimize data orchestration workflows using tools like Airflow, Databricks, or Prefect.• Ensure compliance with data governance, privacy, and security policies.

Required Skills & Qualifications:•

Bachelors/masters degree in computer science, Data Engineering, or a related field.• 7+ years of experience in data engineering, including data pipeline and data lake design.• Strong proficiency in Python, SQL, and Pyspark for data processing and ML integration.• Hands-on experience with cloud services AWS (Glue, Redshift, S3, EMR, Lambda), Azure (Data Factory, Synapse), or GCP (Big Query, Dataflow).• Solid understanding of AI/ML lifecycle, including model training, validation, and deployment.• Experience with MLOps tools such as MLflow, Sage Maker, Kubeflow, or Vertex AI.• Knowledge of data orchestration (Airflow, Databricks Workflows) and CI/CD pipelines for data systems.• Familiarity with version control (Git), containerization (Docker), and infrastructure-as-code (Terraform).• Strong analytical, problem-solving, and communication skills.

Good to Have:•

Experience with GenAI, LLM fine-tuning, or vector databases (Pinecone, FAISS, Chroma DB).• Exposure to data observability and lineage tools (Monte Carlo, Data band, etc.).• Prior experience working in Agile / DevOps environments.