AI/ML ENGINEER

Job Summary:

AI/ML Engineers play a pivotal role in the union of data, systems, and computer sciences. They work closely with a multidisciplinary team, including clinicians, user experience designers, product managers, IT professionals, and external partners, to develop and deploy effective, efficient, and ethical AI/ML solutions into clinical practice to enhance patient care and operational efficiency. As an AI/ML Engineer, you may work on the full spectrum of the AI life cycle from ideation to production. You understand the clinical environment well, including workflows, challenges, and requirements of healthcare providers and patients. You will leverage advanced techniques in AI/ML to analyze vast amounts of healthcare data, including patient records, medical imaging, and genomic information, to develop AI solutions that meet clinical needs and are integrated smoothly into clinical processes. You will develop, integrate, and standardize software components and create, maintain, and follow quality system procedures. You will work on the engineering of systems that are pivotal to developing and deploying these solutions, which encompass everything from design requirements, development, component creation, verification, non-clinical validation, and risk mitigation to ensure our digital health technology products meet and exceed regulatory requirements and set new benchmarks for safety and effectiveness in clinical settings. Your expertise will also extend to facilitating consistent and automated AI software solution development and releases through the design, testing, and maintenance of tools and associated CI/CD pipelines.

Job Responsibilities:

• Working on component design, development, integration, and standardization to create AI-driven solutions that seamlessly integrate into clinical practice to enhance patient care and clinic operations.
• Collaborating with a multidisciplinary team, including clinicians, user experience designers, product managers, and IT professionals, to understand user needs, workflows, and clinical requirements and assess feasibility. Translating user feedback and requirements into design concepts and usability specifications for AI solutions.
• Interpreting/analyzing data to inform strategic decisions and communicate complex findings in easily understandable terms to bridge the gap between AI technologies and clinical applications.
• Leveraging machine learning techniques such as deep learning, natural language processing, computer vision, large language models, etc., to design, develop, and deploy end-to-end AI solutions for healthcare applications.
• Participating in the engineering of systems crucial for developing and deploying AI solutions.
• Facilitating consistent and automated AI software solution development and releases through the design, testing, and maintenance of tools and associated CI/CD pipelines.
• Contributing to implementing the best practices and standards for AI development and deployment methodologies, tools, and platforms.
• Providing training and education to healthcare staff on the use of AI tools and technologies.

Skills:

Required Skills & Experience:

• Experience applying AI and machine learning in production environments, showcasing an understanding of healthcare technology.
• Skill in cloud infrastructure environment and software development tools.
• Experience working with large, complex, and heterogeneous data sets, preferably in healthcare.
• Skill in AI/ML techniques and frameworks.
• History of collaborating across diverse teams and effectively communicating complex technical concepts to non-technical stakeholders.
• Familiarity with best practices in data engineering, data science, AI Engineering, and the MLOps communities.
• Strong interpersonal, communication, and time management skills.

Preferred Skills & Experience:

• A Ph.D. in engineering, computer science, health science, or a related analytical/quantitative field is preferred.
• Expertise in AI/ML techniques and frameworks, such as deep learning, natural language processing, and Generative AI, with proficiency in tools like Python, TensorFlow, PyTorch, sci-kit-learn, Keras, etc.
• Knowledge of the healthcare domain, including clinical workflows, electronic health records, medical terminologies, regulatory requirements, and industry standards.
• Familiarity with systems or quality engineering best practices, regulatory standards, and compliance framew…