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.
Build explainable, evidence-backed AI pipelines that transform clinical decision-making through transparent and trustworthy reasoning systems.
Organization: Confidential client in healthcare technology; the name will be shared with shortlisted candidates before the client interview.
Location: Remote - open to candidates in the Middle East or between the timezone of GMT+3 - GMT+8
Role Type: Full-time | Employee | Reports to: AI Team Lead
The opportunity
Our client is a mission-driven startup building cutting-edge clinical reasoning systems that transform how healthcare decisions are made. They are looking for an Intermediate/Senior AI Engineer to develop explainable, evidence-backed AI pipelines that integrate patient data, medical knowledge, and contextual reasoning. In this role, you will design, implement, and optimize AI systems that deliver transparent, clinically relevant insights, collaborate with cross-functional teams including clinical experts and engineers, and help solve the "black box" problem in healthcare AI by ensuring every clinical recommendation is backed by a traceable, evidence-based reasoning path.
About the organization
Our client is a fast-paced, high-impact startup focused on advancing clinical AI through intelligent, user-centered design. It helps healthcare providers make more informed decisions by integrating large language models, knowledge graphs, and clinical reasoning systems. The engineering team is currently focused on scaling robust AI infrastructure that balances scientific rigor with real-world clinical utility, emphasizing transparency, scalability, and measurable impact.
What you will do
- Own the development of RAG pipelines, embeddings, vector databases, and prompt optimization strategies to ensure accurate retrieval and generation of clinical insights.
- Deliver modular, debuggable Python code with strict version control practices, adhering to software architecture best practices for scalable AI systems.
- Partner with clinical and product teams to integrate domain-specific constraints into model behavior, ensuring outputs meet scientific and regulatory standards.
- Use modern AI coding platforms (e.g., Claude/Claude Code) to accelerate development cycles while maintaining code quality and maintainability.
- Identify and address bottlenecks in distributed inference/training workflows, leveraging quantization and model sharding techniques for efficiency.
- Communicate technical trade-offs, system design decisions, and progress clearly to both technical and non-technical stakeholders.
What you bring
- Demonstrated ability to build production-grade AI/ML systems, shown through 2–3 years of industry experience in similar roles or equivalent impactful projects.
- Advanced degree in Data Science, Computer Science, Bioengineering, Computational Mathematics/Physics/Chemistry/Biology, or a related field.
- Strong proficiency in Python, including asynchronous programming (asyncio), parallelization strategies, and Docker-based cloud-native workflows.
- Experience designing and consuming REST/gRPC APIs for backend integration within complex system architectures.
- Solid understanding of basic statistics up to hypothesis testing, applied to validate model performance and statistical significance.
- Proficiency with leading AI coding assistants like Claude/Claude Code, demonstrating efficient development workflows using these tools.
Helpful, but not essential
- Practical experience with Large Language Models (LLMs), context engineering, and advanced prompt optimization techniques.
- Knowledge of parameter-efficient fine-tuning (PEFT) methods such as LoRA and QLoRA.
- Experience deploying models on 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 specifically for clinical decision support.
- Exposure to classical and modern NLP techniques applied in healthcare or biomedical domains.
How the engagement works
Contracting party: You will contract directly with Apricot, which will manage contracting, invoicing/payroll, payments, and administrative support. Day to day, you will work with the client’s AI engineering team and report to the Engineering Lead or Head of AI. Planned check-ins are scheduled for month 1 and month 3 to see how the engagement is working and help address any issues.
About Apricot
Apricot is a nonprofit sourcing firm connecting displaced and underserved professionals from Palestine and the wider MENA region with global employment opportunities. We combine a clear social-impact mission with fast, high-quality recruitment delivery for international clients.
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