IMO-online is hiring a Staff AI / MLOps Engineer to own the end-to-end machine learning lifecycle for production AI systems. In this technical leadership role, you will design, build, deploy, operate, and evolve scalable, reliable, and observable AI-powered systems in real-world clinical environments. We combine strengths in software development, artificial intelligence, and clinical expertise to create solutions that enhance health information access and improve patient outcomes.
What You'll Do
- Own the full ML lifecycle, including data ingestion, training, validation, deployment, monitoring, retraining, and retirement.
- Transition AI/ML prototypes into scalable, production-ready systems with CI/CD pipelines, automation, and observability.
- Lead system design and architecture discussions, providing guidance on ML systems, MLOps, and AI infrastructure.
- Develop and maintain AI-driven applications and inference services, optimizing for performance, scalability, reliability, and cost.
- Integrate LLMs, generative AI, and NLP solutions into IMO Health products, focusing on unstructured clinical data.
- Implement monitoring, alerting, logging, and dashboards to ensure model quality, detect drift, and maintain operational SLAs.
- Build, maintain, and optimize CI/CD pipelines, automation scripts, and Infrastructure-as-Code for production ML systems.
- Apply containerization and cloud infrastructure best practices to manage production environments.
- Mentor and guide engineers, enforce technical standards, and drive reduction of technical debt.
- Conduct root cause analysis of production defects and implement durable fixes.
- Advocate for non-functional requirements (availability, scalability, reliability, maintainability) and design systems accordingly.
- Collaborate cross-functionally with Product, Data Science, Architecture, and Engineering teams to align AI solutions with business goals.
What We're Looking For
- 8+ years of professional experience in software engineering, AI/ML engineering, or related roles, building and operating production-grade systems.
- Bachelor’s or Master’s degree in Computer Science, Engineering, or a related technical field (or equivalent experience).
- Strong foundation in computer science fundamentals (data structures, algorithms, design patterns, operating systems, networking).
- Expert-level coding skills in Python or Java, with a strong emphasis on production-quality software engineering practices.
- Hands-on experience owning ML systems in production, including deployment, monitoring, retraining, and optimization.
- Experience designing and operating CI/CD pipelines, automation, and observability for ML systems.
- Deep experience with cloud platforms (AWS or Azure), containerization, and Infrastructure-as-Code.
- Experience with MLOps tools and workflows (e.g., MLflow, SageMaker, Kubeflow).
- Experience integrating and deploying LLMs, generative AI, and agentic systems in production environments.
- Working knowledge of NLP concepts (tokenization, embeddings, classification, sequence modeling).
- Experience with Elasticsearch and vector databases for embedding-based search and retrieval.
- Proven ability to translate business needs into scalable, reliable technical solutions, balancing technical debt and delivery velocity.
- Strong system design skills for high-performance, distributed, and scalable systems.
- Excellent communication and collaboration skills across cross-functional, distributed teams.
- Self-starter who can operate autonomously and own complex systems end to end.
Nice to Have
- Experience with clinical or healthcare AI applications.
- Familiarity with Hugging Face, PyTorch, TensorFlow, or other modern ML frameworks.
- AWS Associate-level certification (Machine Learning Engineer or Solutions Architect).
Technical Stack
- Languages: Python, Java
- Cloud: AWS, Azure
- Containers & Orchestration: Docker, Kubernetes
- MLOps: MLflow, SageMaker, Kubeflow
- AI/ML: LLMs, Generative AI, NLP
- Data Stores: Elasticsearch, Vector databases
Team & Environment
This role is part of the Software Engineering organization, partnering closely with data scientists, product teams, and platform engineers.
Benefits & Compensation
- Comprehensive benefits package.
- Base pay is determined by job level, role requirements, and candidate's experience, skills, and location. It excludes bonuses or sales incentives.
IMO-online is an equal opportunity employer.
