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Role Description

This role involves owning the entire machine learning lifecycle—from data ingestion and model training to deployment, monitoring, optimization, and retraining in production. This role bridges research and product by operationalizing AI models, ensuring they scale reliably, and building the infrastructure that enables innovation in clinical data processing.

• Own the full ML lifecycle, including data ingestion, model training, validation, deployment, monitoring, retraining, and retirement.
• Transition AI/ML models from prototypes into scalable, production-ready systems.
• Build, deploy, and maintain CI/CD pipelines for ML 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 to improve efficiency, consistency, and reliability in retraining and deployment workflows.
• Integrate large language models (LLMs), generative AI, and NLP solutions into IMO Health’s Clinical AI products, focusing on unstructured clinical data.
• Develop scalable inference pipelines and APIs to deliver AI capabilities to customer-facing solutions.
• Apply containerization (Docker, Kubernetes) and Infrastructure-as-Code to manage production environments.
• Implement monitoring, alerting, and performance dashboards to ensure model quality, detect drift, and maintain operational SLAs.
• Optimize deployed models for latency, throughput, reliability, and cost efficiency.
• Participate in system design and architecture discussions, providing expertise in MLOps and AI deployment best practices.
• Collaborate in an Agile environment with cross-functional teams, aligning technical solutions with product and business goals.

Qualifications

• 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, maintaining, and scaling 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, monitoring, and observability for ML systems.
• Working knowledge of NLP concepts (tokenization, embeddings, classification, sequence modeling); healthcare domain exposure is a plus.
• Experience fine-tuning and deploying LLMs and generative AI solutions.
• Strong problem-solving skills with the ability to design scalable, reliable, and maintainable ML systems.
• Excellent communication and collaboration skills in cross-functional, distributed teams.
• Self-starter with the ability to work independently and contribute from day one.

Requirements

• 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).

Benefits

Compensation at IMO Health is determined by job level, role requirements, and each candidate’s experience, skills, and location. The listed base pay represents the target for new hires with individual compensation varying accordingly. These figures exclude potential bonuses or sales incentives, which may also be part of the total compensation package. Our recruiter will provide additional details during the hiring process. IMO Health also offers a comprehensive benefits package. To learn more, please visit IMO Health’s Careers Page .