Responsibilities
- Design and implement AI/ML pipelines that solve real-world problems in life sciences (e.g., territory alignment, rep scheduling, patient segmentation, incentive compensation optimization).
- Work closely with domain consultants to understand client requirements and develop tailored AI solutions.
- Lead efforts in data ingestion, feature engineering, and model development across structured and unstructured data sources (e.g., sales data, KOL feedback, medical claims).
- Build and deploy scalable AI services and LLM-based copilots (e.g., for field force, MSLs, or commercial analytics teams).
- Implement GenAI and Agentic AI frameworks for commercial automation.
- Conduct technical reviews and provide mentoring to junior AI team members.
- Collaborate with DevOps and cloud teams to deploy models on AWS/Azure.
- Present technical concepts and outcomes to internal stakeholders and client teams.
Requirements
- At least 7 years of experience in AI/ML engineering.
- Proficient in Python (NumPy, pandas, scikit-learn, PyTorch/TensorFlow, LangChain, Hugging Face).
- Experience building and deploying machine learning models in production.
- Strong understanding of MLOps tools and practices (e.g., MLflow, Docker, Git, CI/CD).
- Exposure to LLMs, transformers, and retrieval-augmented generation (RAG) techniques.
- Experience working with cloud environments (AWS, Azure preferred, Databricks).
- Excellent communication skills with the ability to explain complex technical ideas to non-technical stakeholders.
- Bachelor's/Master's in Computer Science, Data Science, Engineering, or related field.
