Description

You will design, build, and maintain data pipelines while productionizing AI/ML solutions.

This role is on-site.

Responsibilities

• Design and maintain ETL/ELT pipelines using AWS services including Glue, Lambda, Step Functions, EMR, and Kinesis.
• Architect and manage data lakes and warehouses using S3, Lake Formation, Redshift, and Snowflake.
• Implement data ingestion (batch and streaming), data quality checks, and system observability.
• Develop and productionize ML and Generative AI models from data exploration through deployment.
• Mentor junior data scientists on advanced ML techniques and GenAI adoption best practices.

Required Skills

• 15+ years of total IT experience with at least 5 years in Data Engineering.
• Strong AWS expertise with S3, Glue, Athena, Lambda, Step Functions, Redshift, and EMR.
• Proficiency in Python (PySpark, boto3, Pandas, NumPy) and SQL.
• Experience with data modelling (Kimball, Data Vault) and CI/CD.
• Proven production implementation of AI/ML models using AWS SageMaker and Bedrock.
• Expertise in Scikit-learn, TensorFlow, and PyTorch.
• Hands-on experience with Generative AI frameworks, LLM orchestration (LangChain), RAG, and vector databases (OpenSearch).
• Bachelor's degree required.

Preferred Skills

• Experience with fine-tuning LLMs, diffusion models, and transformer-based architectures.
• Familiarity with model governance, bias detection, and AI ethics frameworks.

Key Skills

Education

Bachelor's degree