Pfizer seeks a Full Stack AI Solutions Engineer to build next-generation knowledge work automation tools integrating LLMs into pharmaceutical research. You will design and deploy full-stack applications with Python and React, implementing RAG systems and conversational AI. Collaborate with chemists and scientists to accelerate drug discovery.
Responsibilities
Design and implement production-grade full stack applications integrating LLM and AI capabilities.
Collaborate with medicinal chemists and biomedical researchers to translate scientific challenges into technical solutions.
Develop scalable backend services using Python frameworks for data processing, embedding generation, vector search, and LLM orchestration.
Create responsive frontend interfaces using React and TypeScript.
Implement retrieval-augmented generation (RAG) systems, conversational AI interfaces, and agentic LLM architectures.
Deploy and maintain production systems on AWS cloud infrastructure.
Integrate semantic search technologies, vector databases and embedding models.
Contribute to development of novel semantic frameworks.
Requirements
PhD in Biology, Chemistry, Pharmacology, Toxicology, Computer Science, or related OR Master's degree + 2+ years experience building AI powered research applications.
Pfizer is an American multinational pharmaceutical and biotechnology corporation that researches, develops, and produces medication and vaccines across fields such as immunology, oncology, cardiology, endocrinology, and neurology. As one of the world's leading biopharmaceutical companies, it collaborates with health care providers, governments, and communities to advance global health.
2+ years programming in Python and TypeScript with production software.
Portfolio of full stack applications with Python backends and React frontends.
Experience with FastAPI and React.js.
Excellent communication and collaboration skills.
Nice to Have
Background in life sciences, pharmaceutical research, drug discovery, or cheminformatics.
Hands-on experience with LLM frameworks (OpenAI API, Hugging Face Transformers, Anthropic Claude).
Practical knowledge of prompt engineering, LLM optimization techniques.
Experience building conversational AI interfaces, chatbots, or agentic systems.
Familiarity with vector databases and semantic search.
Familiarity with MongoDB, PostgreSQL.
Experience with PyTorch.
Experience with AWS cloud infrastructure and DevOps.
Knowledge of Docker.
Experience with CI/CD pipelines (GitHub Actions, Jenkins, GitLab CI).
Tech Stack
PythonLLMRAGVector DatabasesReactPyTorchTypeScriptAWSFastAPIPostgreSQLDockerGitHub ActionsHugging Face TransformersMongoDBOpenAI APISemantic SearchAnthropic Claude