Senior Lead, Machine Learning Engineer

Responsibilities

• Lead the development of AI systems from concept to deployment, leveraging state-of-the-art models and techniques to extract meaning, sentiment, intent, and other linguistic features from conversational data.
• Work closely with product managers, designers, and clinical experts to identify opportunities for AI-driven innovation that align with product goals and enhance user experiences.
• Enable rapid A/B testing to continuously refine AI-driven features. Analyze and iterate on model performance to optimize user engagement, product outcomes, and overall system efficiency.
• Develop scalable pipelines and systems to deploy AI models across various environments, ensuring seamless integration with existing product infrastructure while focusing on performance, efficiency, and scalability.
• Establish frameworks to monitor and evaluate the performance of AI applications. Continuously improve accuracy, relevance, and efficiency by setting clear success metrics for each model.
• Build privacy-by-design AI systems with appropriate anonymization, encryption, and access control mechanisms.
• Hire, lead, and mentor a team of AI/ML engineers, promoting a collaborative and innovative environment. Provide guidance on best practices and encourage the adoption of cutting-edge AI/ML techniques.
• Document research findings, methodologies, and technical specifications to ensure effective knowledge sharing and cross-team collaboration.

Skillset

• 8+ years of experience in software development with a strong focus on AI/ML technologies and operations. Proven experience implementing LLM-based operations like RAG (Retrieval Augmented Generation), fine-tuning existing models, and self-hosting open-weight models.
• Proficient in backend development with experience in programming languages like Javascript, Python, Java, or similar. Deep understanding of relational databases, data warehouses, and SQL for data exploration and analysis.
• Driven individual eager to contribute to Noora Health by building and meticulously fine-tuning bespoke machine learning models. Hands-on experience in model training and optimization is a major asset.
• Experience in deploying and managing applications on cloud platforms like Google Cloud (or similar). Expertise in Kubernetes, Docker, and CI/CD pipelines for efficient deployment, monitoring, and management of machine learning models.
• Familiarity with AI model monitoring and observability tools such as Langfuse, Langsmith, Lunary, or other relevant platforms to ensure model performance and reliability.
• Experience with automated testing tools like Jest, Mocha, or similar testing frameworks to ensure the robustness and quality of AI-driven solutions.
• An advanced degree in computer science, information science, data science, mathematics or equivalent hands-on experience in programming.
• Strong analytical, problem-solving, and communication skills, with a keen attention to detail, self-motivation, and the ability to work effectively both independently and collaboratively within a team environment.