Senior AI Engineer

Role Overview We are hiring a Senior Data Scientist / AI Engineer to design, build and scale production-grade AI solutions, with a strong focus on Generative AI, NLP, and healthcare-related use cases. This role is suited for a hands-on technical leader who can translate complex business and domain problems into reliable AI systems that create value, work closely with cross-functional teams, and mentor high-performing data science talent across multiple geographies. The ideal candidate brings strong machine learning fundamentals, deep experience with NLP and transformer-based models, practical exposure to LLMs and RAG systems, and the ability to deliver measurable business and operational impact in real-world environments. Key Responsibilities - Design, build, and deploy end-to-end AI and machine learning solutions, with a focus on GenAI, NLP, and healthcare applications. - Develop and productionize LLM-based workflows, including prompt engineering, evaluation frameworks, fine-tuning approaches, and Retrieval-Augmented Generation systems. - Translate ambiguous business and healthcare problems into structured data science solutions with clear success metrics. - Own the full model lifecycle, including data preparation, experimentation, validation, documentation and articulation of results, deployment, monitoring, and continuous improvement following RAI guidelines. - Work with large-scale structured and unstructured data, including clinical, operational, claims, member, provider, or other healthcare-related datasets. - Partner with product, engineering, business, clinical, and compliance stakeholders to ensure solutions are scalable, explainable, secure, and aligned with business needs. - Lead, mentor, and develop a team of data scientists, and AI engineers, setting high standards for technical quality, analytical rigor, and delivery discipline. - Drive best practices in model development, code quality, documentation, reproducibility, and responsible AI. - 8+ years of experience in developing and implementing end-to-end solutions using Machine Learning and AI tools. - 5+ years of hands-on experience in NLP, deep learning, and transformer-based models. - 2+ years of practical experience building end-to-end Generative AI solutions, including LLM workflows, fine-tuning, evaluation, and RAG-based systems. - Strong proficiency in Python and PySpark is mandatory. - Proven experience building and deploying production-grade ML or AI systems at scale. - Strong business acumen with the ability to convert complex business problems into practical AI solutions that deliver measurable impact. - 2+ years of experience leading, mentoring, or managing high-performing data science teams is highly desirable. - Excellent written and verbal communication skills, with the ability to explain complex technical concepts to both technical and non-technical stakeholders. - Experience working in a matrix organization. Preferred Qualifications - Experience in the healthcare domain is highly desirable, especially in areas such as clinical AI, payer/provider analytics, population health, claims, care management, medical operations, or health data platforms. - Experience working with healthcare data standards, privacy requirements, regulated environments, or responsible AI considerations in healthcare. - Hands-on experience with Azure, Databricks, or equivalent cloud and data platforms. - Working knowledge of MLOps, CI/CD for ML, model monitoring, model governance, and scalable deployment patterns. - Experience optimizing AI systems for accuracy, performance, latency, reliability, cost, and maintainability. - Exposure to multimodal AI, knowledge graphs, medical text analytics, or clinical decision support use cases is a plus. What Good Looks Like - Strong technical depth combined with practical judgment. - Ability to operate in ambiguous environments and bring structure to complex problems. - High ownership mindset with a focus on outcomes, not just models. - Clear communication, strong stakeholder management, and the ability to influence across teams. - Passion for building AI solutions that are robust, responsible, and meaningful in a healthcare context.