Staff AI Engineer: IMO Health

IMO Health combines software development, AI, and clinical expertise to create AI-driven solutions that enhance access to reliable health information and improve patient outcomes.

They are seeking a Staff AI Engineer to join their Software Engineering team. This role is critical for bridging the gap between theoretical AI capabilities and practical implementation. You will be responsible for operationalizing AI models and building AI systems that are scalable, robust, and reliable in real-world environments, with an emphasis on strong software engineering principles.

Responsibilities
Collaborate with cross-functional teams to transition AI/ML models from prototypes into scalable, production-ready systems.

Lead system design and architecture discussions, bringing expertise in AI engineering, MLOps, and AI deployment best practices.

Write high-quality source code, detailed documentation, and high-level technical designs.

Develop and maintain AI-driven applications, ensuring high performance, scalability, reliability, and security (optimizing for latency, throughput, and cost).

Integrate Large Language Models (LLMs), generative AI, and NLP solutions into IMO Health’s products, with a focus on unstructured clinical data.

Apply containerization (Docker, Kubernetes) and Infrastructure-as-Code (IaC) to manage production environments.

Implement creative solutions to technical challenges and conduct root cause analysis on defects.

Mentor junior developers and champion technical standards.

Requirements
Required Qualifications:

8+ years of professional experience in software engineering, AI/ML engineering, or related roles, building production-grade web applications.

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.

Experience in developing AI-powered applications, including LLM integration, prompt engineering, and agentic concepts.

Experience fine-tuning and deploying LLMs and generative AI solutions in production.

Solid hands-on experience with cloud platforms (AWS or Azure), containerization (Docker, Kubernetes), and IaC.

Experience with MLOps tools and workflows (e.g., MLflow, SageMaker, Kubeflow) and CI/CD pipelines.

Working knowledge of NLP concepts (healthcare domain exposure is a plus).

Experience with Elasticsearch and vector databases for embedding-based search and retrieval.

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).