The Role
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.
Summary Generated by Built In
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.
Skills Required
- Advanced degree in Data Science, Computer Science, Bioengineering, Computational Mathematics/Physics/Chemistry/Biology, or related field.
- 2-3 years of industry experience in similar AI/ML roles (preference given).
- Experience using AI coding platforms such as Claude/Claude Code and developing with these tools.
- Experience with RAG pipelines, embeddings, vector databases, and prompt optimization.
- Strong Python development skills, including modular code, debugging, and version control (Git).
- Understanding of quantization, model sharding, and 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 methods such as LoRA and QLoRA.
- Experience with cloud LLM platforms (Amazon Bedrock, Azure OpenAI, Google Vertex AI).
- Familiarity with agentic AI frameworks (LangGraph, AutoGen, 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/biomedical domains.
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The Company
What We Do
Apricot International is a platform that connects companies with exceptional talent from the MENA region, including Palestine, focusing on areas often overlooked by the global industry. They help startups scale by providing top talent in engineering, operations, and sales, handling HR and offering remote job opportunities.
