AI ML Engineers

Key Responsibilities Design, build, and deploy ML and GenAI solutions across multiple use cases (e.g., LLM-based search, document summarization, predictive modeling). Develop and maintain ML pipelines using MLflow, Databricks, and AWS Sagemaker. Implement APIs and microservices for AI model inference using FastAPI and containerization strategies. Work with large structured and unstructured datasets (CSV, JSON, PDFs, EMRs, clinical reports). Optimize data processing using PySpark, SQL, and RDS (AWS). Integrate foundation models and LLMs (e.g., OpenAI, Cohere, Claude, AWS Bedrock) into production workflows. Monitor, retrain, and maintain models in production, ensuring high availability, reliability, and performance. Collaborate with cross-functional teams (Data Engineering, Cloud, Product) to ensure scalable deployments. Document processes, model cards, data lineage, and performance reports for governance and compliance. Required Skills Qualifications 5+ years of hands-on experience in Machine Learning and AI engineering. Strong Python programming skills with experience in FastAPI, PySpark, and SQL. Hands-on experience with MLflow for experiment tracking, model versioning, and deployment. Strong knowledge of Generative AI and LLMs: prompt engineering, fine-tuning, and model integration. Experience working on Databricks (Delta Lake, ML runtime) and AWS Cloud services (S3, EC2, Lambda, RDS, Bedrock). Familiarity with GitLab CI/CD, containerization (Docker), and cloud DevOps practices. Deep understanding of machine learning workflows including data preprocessing, feature engineering, model selection, training, tuning, and deployment. Preferred / Nice to Have Experience in Life Sciences or Healthcare domains: clinical data, RWD/RWE, regulatory AI, etc. Familiarity with data privacy regulations (HIPAA, GDPR) or GxP practices in AI workflows. Experience working with vector databases (e.g., FAISS, Pinecone) and retrieval-augmented generation (RAG). Knowledge of performance monitoring, model drift detection, and responsible AI practices.