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Role Description

This role involves owning the end-to-end machine learning lifecycle for production AI systems.

• 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 (Docker, Kubernetes) 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.

Qualifications

• 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); healthcare exposure is a plus.
• 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.

Requirements

• 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).

Benefits

• Compensation range: $170,000 - $250,000 a year.
• Comprehensive benefits package.