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Metro location, mid-level AI title, and common experience band increase applicant density.
Core LLM and MLOps skills transfer broadly, though pharma validation requirements add domain sensitivity.
Explicit 3-7 years plus mandatory LLM, ML, cloud, and MLOps skills make filters strict.
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Develop, optimize, and deploy production-grade LLM-powered AI applications focusing on Retrieval-Augmented Generation (RAG) pipelines and agentic workflows for healthcare and pharmaceutical clients.
Fine-tune AI models using techniques like Quantization and PEFT/LoRA, ensuring compliance with data privacy and validation requirements in life sciences.
Deploy and maintain models in production using Docker and Kubernetes, implement monitoring for key metrics, and maintain CI/CD pipelines for ML workflows.
3-7 years total experience in Software Engineering or Data Science, with at least 2 years in deploying AI/ML models in production environments.
Expert-level Python programming and proficiency in deep learning frameworks such as PyTorch or TensorFlow.
Hands-on experience with building LLM-based applications and vector database management.
Strong experience with cloud platforms (AWS/Azure/GCP) and containerization technologies like Docker and Kubernetes.
Experienced in AI development within regulated or sensitive domains, preferably healthcare or pharmaceuticals, with familiarity in data privacy and validation processes.
Proficient in engineering practices emphasizing testing, modularity, and documentation in ML systems.
Skilled in advanced AI deployment techniques including RAG pipelines, fine-tuning strategies, and CI/CD automation for machine learning.