Full Stack AI Solutions Engineer

About the role

This innovative role combines cutting-edge software engineering with artificial intelligence to build next-generation knowledge work automation tools that directly impact pharmaceutical research outcomes. You will work at the forefront of applied AI, collaborating closely with Medicinal Chemists and Biomedical Scientists to design, develop, and deploy intelligent systems that enhance productivity and accelerate decision-making in drug discovery.

Responsibilities

• Design and implementation of production-grade full stack applications that seamlessly integrate LLM and AI capabilities into scientific workflows, enabling researchers to leverage cutting-edge artificial intelligence in their daily work
• Direct collaboration with medicinal chemists, biomedical researchers, and domain experts to deeply understand requirements, translate scientific challenges into technical solutions, and deliver intuitive, user-centric applications
• Development of scalable backend services using Python frameworks for data processing, embedding generation, vector search, and LLM orchestration that power AI-driven research tools
• Creation of responsive, modern frontend interfaces using React and TypeScript that provide exceptional user experiences and dramatically enhance researcher productivity
• Implementation of retrieval-augmented generation (RAG) systems, conversational AI interfaces, and agentic LLM architectures that automate knowledge work in pharmaceutical research
• Deployment and maintenance of production systems on AWS cloud infrastructure with emphasis on reliability, performance, scalability, and optimal user experience
• Rapid iteration and continuous improvement based on user feedback, evolving scientific needs, and emerging AI/LLM capabilities
• Integration of semantic search technologies, vector databases, and embedding models to enable intelligent information retrieval
• Contribution to the development of novel semantic frameworks and conceptual research that advances AI-driven knowledge systems for drug discovery
• Strengthen cross-functional collaboration and knowledge sharing through clear communication, documentation, and engagement with the broader research community

Qualifications

• PhD in Biology, Chemistry, Pharmacology, Toxicology, Computer Science, or a related technical discipline OR Master’s degree and 2+ years of experience building AI powered research applications
• Strong technical problem solving skills with ability to translate complex scientific requirements into elegant technical solutions
• 2+ years of programming experience in Python and TypeScript with experience building production-quality software
• Portfolio of production-grade full stack applications with Python backends and React frontends (GitHub portfolio required)
• Experience with modern web frameworks including backend frameworks (FastAPI) and frontend frameworks (React.js)
• Excellent communication and collaboration skills with experience working effectively in cross-functional teams
• Strong communications skills—written, verbal and presentation

Preferred Qualifications

• Background or demonstrated interest in life sciences, pharmaceutical research, drug discovery, or cheminformatics
• Hands-on experience with LLM frameworks and libraries (OpenAI API, Hugging Face Transformers, Anthropic Claude)
• Practical knowledge of prompt engineering, LLM optimization techniques, and best practices for building LLM-powered applications
• Experience building conversational AI interfaces, chatbots, or agentic systems with autonomous decision-making capabilities
• Familiarity with vector databases and semantic search technologies for similarity search and retrieval
• Familiarity with MongoDB, PostgreSQL or other modern database technologies for data persistence and retrieval
• Demonstrated experience implementing, training, fine-tuning, or applying deep learning models to natural language processing or computer vision problems using PyTorch
• Familiarity with AWS cloud infrastructure and services (EC2, S3, CloudFormation, RDS) and DevOps best practices
• Experience with containerization and orchestration tools (e.g. Docker) for scalable application deployment
• Experience with CI/CD pipelines, automated testing, and deployment automation tools (GitHub Actions, Jenkins, GitLab CI)