Immediate Joiners Preferred
Role Overview :
We are looking for experienced AI and Data Science professionals to design, build, and deploy production-grade machine learning and Generative AI solutions across healthcare and life sciences use cases. The role spans predictive modeling, causal inference, GenAI system engineering, and end-to-end MLOps deployment.
Key Responsibilities
- Develop ML models for patient and HCP prediction, segmentation, and targeting.
- Work with longitudinal healthcare and US pharma datasets to engineer high-quality features.
- Apply causal inference, deep learning, and advanced ML techniques to solve complex business problems.
- Design, build, and maintain scalable ML pipelines and production ML systems.
- Engineer and deploy production-grade Generative AI and Agentic AI solutions.
- Implement evaluation frameworks including human-in-the-loop review, hallucination detection, toxicity checks, and LLM-based evaluation.
- Monitor model performance, latency, accuracy, and drift in production environments.
- Build and manage MLOps, ETL pipelines, CI/CD workflows, and deployment infrastructure.
- Collaborate with data, engineering, and product teams to deliver enterprise-ready AI solutions.
Required Skills
- Strong experience in machine learning, deep learning, and predictive analytics.
- Hands-on expertise in feature engineering with healthcare or longitudinal datasets.
- Knowledge of causal inference methodologies and experimentation design.
- Experience building ML pipelines and productionizing models via MLOps.
- Proven ability to design and deploy GenAI and Agentic AI applications.
- Familiarity with evaluation, benchmarking, and performance monitoring of AI systems.
- Experience with DevOps, CI/CD, and production deployment workflows.
Preferred Experience
- Exposure to US healthcare/pharma data ecosystems.
- Experience delivering AI POCs, MVPs, and scalable enterprise deployments.
- Strong problem-solving mindset with ability to translate business needs into AI solutions.
(ref:hirist.tech)
