WHAT YOU’LL DO:
- Collaborate with cross-functional teams to transition AI/ML models from prototypes into scalable, production-ready systems.
- Build, deploy, and maintain CI/CD pipelines for machine learning models, ensuring reproducibility, scalability, and reliability.
- Design and implement cloud-based infrastructure (AWS, Azure, or equivalent) for training, inference, and monitoring of AI models.
- Automate repetitive ML lifecycle tasks, improving efficiency and consistency in retraining and deployment workflows.
- Integrate large language models (LLMs), generative AI, and NLP solutions into IMO Health’s Clinical AI products, with a focus on unstructured clinical data.
- Develop scalable inference pipelines and APIs to deliver AI capabilities into customer-facing solutions.
- Apply containerization (Docker, Kubernetes) and Infrastructure-as-Code to manage production environments.
- Participate in system design and architecture discussions, bringing expertise in MLOps and AI deployment best practices.
- Ensure performance, reliability, and security of deployed models, optimizing for latency, throughput, and cost.
- Collaborate in an Agile environment with cross-functional teams, aligning technical solutions with product and business goals.
WHAT YOU’LL NEED:
- 5+ years of professional experience in software engineering, AI/ML engineering, or related roles.
- Bachelor’s or Master’s degree in Computer Science, Engineering, or a related technical field (or equivalent experience).
- Strong coding skills in Python or Java, with experience in software engineering best practices.
- Hands-on experience deploying and maintaining ML models in production environments.
- Proficiency with cloud platforms (AWS or Azure), containerization, and Infrastructure-as-Code.
- Experience with MLOps tools and workflows (e.g., MLflow, SageMaker, Kubeflow).
- Familiarity with CI/CD pipelines, automation, and monitoring for machine learning systems.
- Working knowledge of NLP concepts (tokenization, embeddings, classification, sequence modeling) — healthcare domain exposure is a plus.
- Experience fine-tuning and deploying large language models (LLMs) and generative AI solutions.
- Strong problem-solving skills with the ability to design scalable, reliable systems.
- Excellent communication and collaboration skills in cross-functional, distributed teams.
- Self-starter with the ability to work independently and contribute from day one.
NICE TO HAVE:
- Experience with clinical or healthcare AI applications.
- Familiarity with Hugging Face, PyTorch, TensorFlow, or other modern ML frameworks.
- Prior exposure to agentic AI and generative AI applications.
- AWS Associate-level certification (Machine Learning Engineer or Solutions Architect).
Top Skills
What We Do
We are a team of dedicated clinical terminologists, data scientists, industry subject matter experts, and informaticists who helped facilitate the evolution from analogue to digital capture of clinical events, the precise code-mapping that simplifies complex workflows, and the translation of unstructured into structured data. We “wrote the digital dictionary” used in every major EHR, and we are leveraging clinical AI to generate insights that expand and deepen our impact across the healthcare ecosystem.
At the end of the day, we don’t make decisions for our clients. We provide them with the digital tools to enable sound decision-making.
Why Work With Us
We are building a clinical intelligence stack—medical ontology, human expertise, and AI—that makes data more useful and more powerful. By enhancing data’s structure, richness, and precision, we reduce noise and error, streamline complexity, and create clarity across the clinical information chain.
Gallery
