Design and implement ETL pipelines using tools like Apache Airflow, Spark, dbt, or AWS Glue to preprocess large-scale data for ML training.
Fine-tune and deploy LLMs (e.g., Llama, GPT variants, BERT) using frameworks like Hugging Face Transformers, LangChain, or vLLM for tasks such as RAG, chatbots, and semantic search.
Develop end-to-end ML workflows, from data ingestion and feature engineering to model training, evaluation, and MLOps deployment with Kubernetes, Docker, and MLflow.
Optimize LLM inference for cost and latency, implementing techniques like quantization, distillation, and vector databases (e.g., Pinecone, FAISS).
Collaborate with data engineers and stakeholders to ensure data quality, compliance (e.g., HIPAA for healthcare), and model performance in production.
Monitor and retrain models using A/B testing and metrics like BLEU, ROUGE, or custom business KPIs.
Contribute to research on emerging LLM architectures and ETL best practices.
Bachelor's or Master's in Computer Science, Data Science, or related field.
4+ years of experience as an ML Engineer, with 2+ years specifically in LLMs and ETL.
Proficiency in Python, PyTorch/TensorFlow, and ETL tools (Airflow, Spark, Kafka).
Hands-on experience with LLM frameworks (Hugging Face, LangChain) and vector stores.
Strong SQL/PL/SQL skills and experience with cloud platforms (AWS, Azure, Google Cloud Platform).
Familiarity with DevOps practices for ML (CI/CD, containerization).
Experience in healthcare AI (e.g., FHIR data processing, de-identification).
Knowledge of MLOps tools like Kubeflow, Seldon, or Ray.
Publications or contributions to open-source LLM/ETL projects.
Certifications: AWS ML Specialty, Google Professional ML Engineer.
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