Description

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

Responsibilities

• Design, build, and maintain ETL/ELT pipelines using AWS data services (Glue, Lambda, Step Functions, EMR, Kinesis).
• Architect and manage data lakes/data warehouses (S3, Lake Formation, Redshift, Snowflake optional).
• Implement robust data ingestion (batch & streaming), data quality checks, and observability.
• Design, 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 a minimum of 5 years in Data Engineering.
• Strong AWS experience with S3, Glue, Athena, Lambda, Step Functions, Redshift, and EMR.
• Proficiency in Python (PySpark, boto3) and SQL; experience with data modelling (Kimball, Data Vault).
• Proven experience in production implementation of AI/ML models using AWS SageMaker and Bedrock.
• Expertise in Python, Pandas, NumPy, Scikit learn, TensorFlow, and PyTorch.
• Hands-on experience with Generative AI frameworks and LLM orchestration tools (e.g., LangChain).
• Experience with Retrieval Augmented Generation (RAG) and vector databases (e.g., OpenSearch).
• Solid understanding of data engineering workflows, including ETL pipelines and feature stores.
• 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