Description

You will design, build, productionize, and operate machine learning and AI solutions supporting U.S. HCP Field Engagement initiatives.

Responsibilities

• Deliver end-to-end ML/AI solutions, covering problem definition, feature engineering, model training, evaluation, deployment, and monitoring.
• Build and deploy NLP and LLM-based solutions, including RAG pipelines, prompt engineering, and predictive analytics models.
• Develop scalable APIs and services for model serving using frameworks like FastAPI or Flask, integrating them with data pipelines.
• Implement MLOps practices, managing experiment tracking, CI/CD, model versioning, and drift monitoring.
• Collaborate with cross-functional teams to ensure solutions meet business, regulatory, and governance requirements while writing high-quality, testable code.

Required Skills

• 5+ years of experience in applied machine learning, AI, or data science software development.
• Strong proficiency in Python and hands-on experience with PyTorch, TensorFlow, Scikit-learn, and Hugging Face.
• Proven experience building and deploying production-grade ML services and APIs.
• Practical MLOps experience, including CI/CD pipelines (e.g., GitHub Actions) and model registries.
• Hands-on experience with LLM workflows: prompt design, fine-tuning, embeddings, and vector databases.
• Solid SQL skills for data wrangling and feature engineering.
• Understanding of statistical modeling, predictive analytics, and model optimization.
• Familiarity with responsible AI, bias detection, and regulatory compliance.
• Experience with AWS, Docker, and Kubernetes.

Key Skills

Education

Any Graduate