AI Data Engineer

Required Skills and Qualifications

• Bachelor's or Master's degree in Computer Science, Data Science, Machine Learning, or related field.

• 5-7 years of hands-on experience in data engineering, with at least 2 years focused on AI/ML workloads.

• Expert proficiency in Python and experience with ML frameworks such as PyTorch, TensorFlow, or scikit-learn.

• Strong experience with Databricks, Apache Spark, and distributed computing for ML workflows.

• Deep understanding of the machine learning lifecycle, including model training, deployment, and monitoring processes.

• Experience with feature engineering, data preprocessing techniques, and ML data pipelines.

• Knowledge of vector databases, embeddings, and similarity search for AI applications.

• Proficiency in SQL for structured and unstructured data management.

• Understanding of data governance, model governance, and AI ethics principles.

• Strong analytical and problem-solving capabilities with attention to data quality.

• Excellent collaboration skills for working with data scientists, ML engineers, and architects.

Preferred/Nice-to-Have Skills

• Experience with generative AI applications, including RAG (Retrieval-Augmented Generation) and fine-tuning.

• Knowledge of LangChain, HuggingFace, or other GenAI frameworks.

• Familiarity with Azure ML, AWS SageMaker, or Google Vertex AI platforms.

• Experience with graph databases (Neo4j, Amazon Neptune) for knowledge graph implementation.

• Understanding of AI model explainability and interpretability techniques.

• Experience with A/B testing frameworks for ML model evaluation.

• Certification in Databricks, AWS, Azure, or GCP AI/ML services.

• Publications or contributions to open-source ML projects