This is a fully remote position, open to applicants in North Carolina.

📋 Description

• Provide demonstrations of concept to showcase the potential and benefits of Generative AI solutions.

• Keep up to date with the latest developments in Generative AI and Machine Learning, applying them to real-world business problems.

• Offer technical guidance and mentorship to team members.

• Present and clarify AI solutions to non-technical stakeholders during client engagements.

• Use consulting principles to create customized AI solutions that meet client requirements.

• Work collaboratively with internal teams and clients in the pharmaceutical and life sciences sectors to tackle business challenges using Generative AI.

• Act as the main contact for client stakeholders on designated projects.

• Lead interdisciplinary teams in the development, deployment, and scaling of AI/ML solutions in production settings.

• Design and implement tailored AI solutions for clients in the pharmaceutical and life sciences fields, from initial use case identification and proof of concept to large-scale production deployment.

⛳️ Requirements

• Significant domain expertise in Life Sciences.

• Proven experience in managing complex client projects, ideally within Life Sciences R&D.

• Strong grasp of AI/ML concepts and contemporary AI architectures, including Generative AI, foundational models, and agentic AI.

• Exceptional analytical and problem-solving abilities.

• Excellent communication and teamwork skills, with the capability to convey intricate technical concepts to non-technical audiences.

• Capability to juggle multiple projects and priorities in a fast-paced, client-oriented environment.

• A keen interest in contributing to a collaborative, team-focused culture.

• An advanced degree in Computer Science, Engineering, Life Sciences, or a related field is preferred.

• Experience with Python and LangChain/LangGraph is preferred.

• Familiarity with deploying machine learning models at scale on cloud platforms such as Azure or AWS is preferred.

• Knowledge of Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), Proximal Policy Optimization (PPO), and Reward Modeling is a bonus.