AI Engineer

We are seeking an Intermediate/Senior AI Engineer to join our remote team in building cutting-edge clinical reasoning systems that transform how healthcare decisions are made. As part of a mission-driven startup, you will contribute to developing explainable, evidence-backed AI pipelines that integrate patient data, medical knowledge, and contextual reasoning to deliver transparent, trustworthy clinical insights. This role is ideal for a technically strong engineer passionate about leveraging AI to solve real-world healthcare challenges, with a focus on transparency, scalability, and impact. You will work closely with cross-functional teams to design, implement, and optimize AI systems that are both scientifically rigorous and clinically relevant.

• Advanced degree in Data Science, Computer Science, Bioengineering, Computational - Mathematics/ Physics/ Chemistry/ Biology, or a related field.
  
• Preference will be given to candidates with 2–3 years of industry experience in similar AI/ ML roles.
  
• Experience in using the latest AI coding platforms like Claude/Claude Code and proficient at development using these tools
  
• Experience with RAG pipelines, embeddings, vector databases, and prompt optimization.
  
• Strong Python development skills, including modular code, debugging, and version control.
  
• Understanding of quantization, model sharding, distributed inference/training.
  
• Basic understanding of software architectures
  
• Experience with REST/gRPC APIs and backend integration.
  
• Familiarity with asyncio and parallelization strategies.
  
• Docker-based workflows and cloud-native concepts.
  
• System design knowledge for scalable AI pipelines.
  
• Good understanding of basic statistics up to hypothesis testing
  

• Practical experience with large language models (LLMs), context engineering, and prompt optimization. 
  
• Knowledge of parameter-efficient fine-tuning (PEFT) methods such as LoRA and QLoRA. 
  
• Experience with cloud LLM platforms including Amazon Bedrock, Azure OpenAI, or Google Vertex AI. 
  
• Familiarity with agentic AI frameworks such as LangGraph, AutoGen, or Crew AI. 
  
• Working knowledge of graph databases (e.g., Neo4j) and knowledge graph reasoning for clinical decision support. 
  
• Exposure to classical and modern NLP techniques applied in healthcare or biomedical domains.