Data Engineering & Data Processing:
Design and develop scalable ETL/ELT pipelines for ingesting, transforming, and processing structured and unstructured data.
Build and optimize data pipelines using Databricks, Spark, SQL, and cloud-native AWS services.
Implement data quality, validation, lineage, and monitoring processes.
Support medallion/Lakehouse architecture patterns including bronze, silver, and gold data layers.
Develop data pipelines to support AI/ML, GenAI, and RAG workloads, including document ingestion and embedding generation workflows.
Machine Learning & Modeling:
Design and implement scalable ML models for classification, regression, clustering, forecasting, and recommendation systems.
Apply advanced techniques including deep learning, ensemble learning, NLP, Generative AI, and LLM-based solutions where applicable.
Conduct model evaluation, tuning, validation, and performance optimization using industry best practices.
Develop and train models within Databricks ML and/or AWS SageMaker leveraging distributed computing and scalable cloud infrastructure.
Build reusable feature engineering and model training pipelines.
Develop Retrieval-Augmented Generation (RAG) solutions integrating LLMs with enterprise knowledge sources and vector databases.
Cloud & MLOps:
Deploy and manage ML and GenAI models using AWS SageMaker and Databricks, including endpoint configuration, monitoring, and retraining workflows.
Utilize Databricks MLflow for experiment tracking, model registry, and deployment automation.
Implement and support vector database solutions for semantic search and RAG architecture.
Collaborate with DevOps and platform teams to implement CI/CD pipelines for ML, GenAI, and data workloads.
Automate operational workflows and optimize cloud resource utilization, scalability, reliability, and security.
Deliverables:
Production-ready ML and GenAI solutions with supporting technical documentation.
Scalable ETL/ELT pipelines and curated datasets.
End-to-end Databricks notebooks, jobs, and workflows.
Feature engineering pipelines and reusable ML components.
RAG pipelines integrated with vector databases and enterprise knowledge sources.
Weekly status reports and participation in Agile sprint ceremonies.
Skills & Qualifications:
8+ years of experience in Data Science, Machine Learning, and Data Engineering.
Strong proficiency in Python, SQL, Spark, and ML libraries such as scikit-learn, TensorFlow, and PyTorch.
Experience with Generative AI, LLM frameworks, prompt engineering, and RAG architecture.
Hands-on experience with vector databases and semantic search technologies.
Hands-on experience with Databricks, MLflow, Delta Lake, and AWS SageMaker.
Experience designing scalable data pipelines and distributed data processing solutions.
Strong understanding of data mining, feature engineering, and data modeling techniques.
Experience with cloud-native AWS data services and orchestration frameworks.
Excellent communication, collaboration, and leadership skills.
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