AI Engineer (Pipelines) - Expression of Interest
Join ThinkMD and help transform global healthcare through innovation
ThinkMD is a high-growth, AI-native health and data company transforming global healthcare through scalable, clinically validated technology. Our mission is to defeat global healthcare inequity by enabling mass distribution of AI-driven clinical decision-support solutions.
Already conducting 22,000+ clinical assessments per week across our partner countries including Zambia, Kenya, Sierra Leone and Cambodia, ThinkMD is rapidly scaling across the developing and low resource countries — providing powerful health intelligence that optimizes patient & clinician experiences, informs national-level health decision making, and drives commercial value.
Backed by world-class venture partners and global health leaders, ThinkMD is not just creating impact—we are commercializing the future of AI-powered healthcare in low-resource settings. Through strategic partnerships with governments, not for profits, private healthcare providers, pharma, and telecom companies, we are positioning ourselves as a market leader in digital health solutions for the Global South and beyond.
We're scaling globally to impact over 1 billion lives by 2030. To support this next phase of innovation and growth, we're seeking a talented AI Engineer (Pipelines) to strengthen our technical capabilities which underpin our products and organizational capabilities.
Ready to help us impact a billion lives by 2030?
Submit your Expression of Interest today!
The Opportunity
As our AI Engineer (Pipelines) at ThinkMD, you will design, implement, maintain, and optimize AI pipelines to support large language models (LLMs) and other AI systems, with a focus on infrastructure and operations. You will set up and manage robust Retrieval-Augmented Generation (RAG) architectures and enable post-training processes such as fine-tuning, RLHF, and model distillation. Working closely with cross-functional teams, you will ensure scalable, efficient, and reliable AI solutions that align with our mission to deliver equitable healthcare.
What You'll Do
Pipeline Development & RAG Architecture
- Design and implement end-to-end production AI pipelines for inference and post-training using LLMs such as Llama, DeepSeek, and Qwen, with a focus on RAG-based architectures
- Build and maintain RAG pipelines that integrate LLMs with external knowledge sources to ensure factual accuracy and up-to-date responses
Infrastructure & Cloud ML Ops
- Configure and optimize infrastructure to support LLMs and post-training processes, including fine-tuning, RLHF, and model distillation
- Manage AI workflows on Google Cloud Platform (GCP), leveraging Vertex AI, Google Kubernetes Engine (GKE), and GPU resource management for high performance
- Deploy and serve LLMs using tools like vLLM and Ray, ensuring low-latency and high-throughput inference
Data Engineering & Pipeline Management
- Develop and manage data pipelines using Google Cloud Storage and BigQuery to support training, evaluation, and RAG workflows
- Automate deployments and manage cloud infrastructure using Python, shell scripting, YAML, and infrastructure-as-code tools (e.g., Terraform)
- Implement monitoring systems and best practices for pipeline performance, scalability, and cost optimization in production environments
- Apply strategies to optimize cloud resource usage and reduce operational costs while maintaining performance
What You Bring
Education & Experience
- Master's or PhD in Computer Science, Electrical Engineering, Statistics, or a related field, with formal training in AI/ML, including coursework in deep learning and/or natural language processing
- 2+ years in AI/ML engineering and cloud ML Ops, with a focus on building and optimizing AI pipelines for production
- Proven expertise in deploying and maintaining RAG-based architectures using LLMs (e.g., Llama, DeepSeek, Qwen)
- Hands-on experience with distributed training and inference on cloud GPUs
Technical Expertise
- Deep Learning Frameworks: Proficiency in PyTorch and Hugging Face Transformers
- Vector Databases & Embeddings: Experience with vector databases (e.g., Pinecone, FAISS, Chroma) and embedding models for RAG workflows
- Tools and Frameworks: Familiarity with Unsloth, vLLM, Ray and Weights & Biases (Wandb) for pipeline development and monitoring
- Cloud Platforms: Extensive experience with GCP, particularly Vertex AI, GKE, and GPU resource management
- Programming: Advanced proficiency in Python, shell scripting, and YAML; knowledge of Terraform is a plus
- Containerization and Orchestration: Expertise in Dockerized workflows and Kubernetes for scalable AI deployments
- Data Engineering: Skills in managing datasets and building data pipelines using Google Cloud Storage and BigQuery
Professional Qualities
- Strong problem-solving abilities
- Excellent communication skills
- Ability to collaborate in a fast-paced, team-oriented environment
Preferred Qualifications
- Experience setting up infrastructure for model post-training, including fine-tuning, RLHF, and distillation
- Knowledge of cost optimization strategies on cloud platforms, such as auto-scaling and resource allocation
- Familiarity with software engineering principles, including Git, CI/CD pipelines, and unit testing
- Contributions to open-source AI/ML projects or publications in conferences like NeurIPS, ICLR, or ICML
What We Offer
- Opportunity to make a meaningful impact on global healthcare inequity
- Collaborative work environment with a mission-driven team
- Competitive compensation and benefits package
- Professional growth and development opportunities
- Flexible work arrangements
Requirements
- Ability to work flexible hours across global time zones with global teams
- Legal work rights in your country of residence
- Clean background check
Ready to help us impact a billion lives by 2030?
Submit your Expression of Interest today!
ThinkMD is an equal opportunity employer committed to building a diverse and inclusive team. We welcome candidates from all backgrounds who share our passion for using technology to transform healthcare accessibility worldwide.