Data Engineer

Data Engineer and Generative AI/ML Specialist

Skill Cluster/Practice: Data Engineering / Emerging Technologies (GenAI)

We are seeking an innovative and results-driven Data Engineer and GenAI/ML Specialist with expertise in large-scale data engineering, LLM-based automation, and the implementation of intelligent systems. The ideal candidate will leverage proven experience to develop robust, compliant, and scalable data solutions, contributing to both data infrastructure and advanced AI applications.

Data Pipeline Architecture: Design, build, and maintain large-scale, high-volume real-time data ingestion and streaming pipelines using technologies like Apache Spark/PySpark, Python, and Kafka on cloud platforms like Databricks, GCP, and AWS.

Generative AI and Machine Learning: Lead the development and deployment of LLM-based automation, Retrieval-Augmented Generation (RAG) pipelines, and AI agent systems (using tools like OpenAI/HuggingFace).

ML/DL Model Productionization: Apply and productionize Machine Learning and Deep Learning models, including building classification models, predictive models, time-series forecasting, and sentiment analysis solutions using frameworks like PyTorch, TensorFlow, and scikit-learn.

Data Modeling and Warehousing: Migrate and re-architect data models to build and maintain data lakes and data warehouses (e.g., Snowflake), streamlining ETL/ELT workflows using tools like dbt and Apache Airflow.

Data Quality and Compliance: Ensure data ingestion and processing workflows adhere to data quality standards, compliance, and data privacy requirements.

Performance Optimization: Optimize SQL and Spark workloads to improve query performance and reduce compute costs.

Required Technical Skills And Experience

Experience and hands-on experience in Data Engineering, Machine Learning, or AI Solution Engineering.

Core Proficiency: Deep proficiency in Python and SQL for data processing, scripting, and optimization.

Big Data & Cloud: Expertise in distributed computing (e.g., Apache Spark/PySpark, Databricks), real-time streaming technologies (e.g., Kafka), and cloud platforms (AWS, GCP, Snowflake).