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.

Requirements

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.

Benefits

IMO Health offers a comprehensive benefits package. To learn more, please visit IMO Health’s Careers Page.

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

machine learning lifecycledata ingestionmodel trainingmodel validationmodel deploymentmodel monitoringmodel retrainingPythonJavaNLP

Soft Skills

problem-solvingcommunicationcollaborationself-starterindependent work

Certifications

Bachelor's degree in Computer ScienceMaster's degree in Computer Science