Design, implement, and optimize explainable clinical reasoning AI systems that integrate patient data and medical knowledge. Build RAG/embedding pipelines, backend integrations, scalable Docker/cloud-native deployments, and ensure transparent, evidence-backed model outputs while collaborating with cross-functional teams.
This is a remote position.
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.
Requirements
- 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
Preferred Skills
- 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.
Benefits
Why Join Us
You will work in a high-impact, fast-paced environment solving complex healthcare AI problems. You will collaborate with a multidisciplinary team and work on state-of-the-art technologies including LLMs, knowledge graphs, and clinical reasoning systems. The role offers significant ownership and opportunities for professional growth. Here is a chance to make a mark in the healthcare space by solving the 'black box' problem in healthcare AI by building systems where every clinical recommendation is backed by a traceable, evidence-based reasoning path.
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