Join us to deploy and implement cutting-edge AI/ML models addressing complex challenges in life science R&D areas including target choice, patient identification, molecule design and clinical trial effectiveness. Collaborate with data scientists and leverage cloud infrastructure to build scalable training pipelines, ensuring rapid iterations and operational excellence. Develop robust backend systems and evaluation frameworks while engaging in technology assessments and co-development with strategic partners to support GenAI products across R&D functions.

Responsibilities:

• Deploy and implement advanced AI/ML models to tackle complex problems in target choice, patient identification, molecule design/chemistry, manufacturing and controls (CMC), and clinical trial effectiveness.

• Design and implement distributed training pipelines for LLMs using tools such as DeepSpeed, ensuring scalability and efficiency.

• Collaborate with Data Scientists in LLM customization: pre-training, fine-tuning, reinforcement learning with human feedback (RLHF), and applying parameter efficient fine-tuning (PEFT) techniques.

• Define and implement repeatable AI/ML pipelines to ensure rapid iterations of data science experiments while leveraging the cloud infrastructure to implement industry best practice in MLOps.

• Model operationalization and monitoring on Azure and GCP infrastructures with close collaboration with the DevSecOps team.

• Build scalable, reusable backend systems including the state-of-the-art agentic frameworks to support GenAI products across R&D functions. Develop robust logging, telemetry, and evaluation harnesses to ensure reliable model performance.

• Technology assessment of external product solutions and co-development with strategic partners

• Create and maintain pipelines for producing training/testing/validation data sets

• Build and maintain model evaluation framework

Why You?

Basic Qualifications:

• BS degree in computer science, engineering, bioinformatics or applied math 

• 5+ years of engineering experience

• Experience working with LLM technologies, including GenAI embedding techniques, modern model architecture, retrieval-augmented generation (RAG), fine tuning/pre-training AI models, and evaluation benchmarks

• Experience in Python, TensorFlow/PyTorch, and scalable ML architectures.

• Experience in Agentic framework, i.e., AutoGen and LangGraph

• Experience in Azure and GCP cloud services

• Experience in full stack software development, and knowledge of software engineering principles around testing, code reviews and deployment.

Preferred Qualifications: 

• MA degree in computer science or engineering

• 10+ years of combined full stack, data engineering, Azure/GCP cloud services, and AI engineering experience

• Experience with graph databases in the context of GraphRAG

• Experience with data lake-house architecture, data catalog, master data management applications 

• Experience in establishing AI/ML and agent best practices, standards, and ethics

• Experience in AI/ML applications in life science domain areas: pre-clinical research, clinical trial design and operation, precision medicine, regulatory science, and CMC.

• Experience in reducing the cost of running a service / capability while maintaining or improving performance efficiencies

• Strong analytical and problem-solving skills, with a passion for shaping AI-driven workflow.

• Strong written and verbal communication skills