Lead Applied AI Engineer

Responsibilities:

• Lead the design, development, and deployment of scalable AI/ML and Agentic AI solutions that align with enterprise strategy and deliver measurable business impact.
• Guide cross-functional teams through complex AI initiatives, ensuring alignment with business objectives and technical feasibility.
• Architect and operationalize advanced AI models including LLMs, SLMs, multi-modal models, and traditional ML using Python and cloud-native platforms such as Azure AI.
• Build and integrate intelligent agents using frameworks like LangChain and Semantic Kernel into enterprise platforms, driving automation and insight generation.
• Apply MLOps best practices to ensure robust, secure, and compliant deployment pipelines using Azure Machine Learning and Azure DevOps.
• Serve as a technical advisor to business stakeholders, translating strategic goals into actionable AI solutions and roadmaps.
• Mentor junior engineers and contribute to the development of AI governance, standards, and best practices across the organization.
• Evaluate and implement emerging GenAI tools and frameworks, fostering innovation and continuous improvement in our AI capabilities.
• Ensure AI systems adhere to privacy, security, and regulatory standards including PHI, PII, GDPR, and HIPAA.
• Collaborate with global teams and enterprise systems (e.g., Power BI, Microsoft Fabric, SAP Datasphere) to integrate AI solutions that enhance decision-making and operational efficiency.

Requirements:

• 8 12 minimum overall experience is required
• Experience in MedTech, medical devices, or regulated healthcare technology is strongly preferred, especially in roles requiring compliance with data privacy and security standards (e.g., GDPR, HIPAA).
• Experience leading complex AI/ML initiatives across multiple functions, with a proven ability to guide teams, influence strategy, and deliver enterprise-scale solutions
• Experience driving innovation,and contributing to the development of functional or cross-functional strategies.
• Bachelor's degree in computer science, Information Systems, Engineering, or related field.
• Master's degree preferred.
• One or more of the following is recommended but not required: Azure AI Engineer Associate, Azure Data Scientist Associate, AWS Certified Machine Learning, Google Cloud Platform Cloud Professional Machine Learning Engineer

Specialized Skills/Technical Knowledge:

• 8 years of experience delivering production-ready AI/ML applications, with demonstrated leadership in cross-functional project environments.
• Advanced proficiency in Python and ML libraries (e.g., scikit-learn, PyTorch, TensorFlow), with expertise in building and optimizing AI models.
• Deep experience with Azure AI/ML tools, including Azure ML, Azure OpenAI, and Azure DevOps; familiarity with other hyperscaler platforms is a plus.
• Proven ability to design and implement intelligent agents using frameworks such as LangChain, Semantic Kernel, or similar.
• Expertise in building ML pipelines, integrating APIs, and working with vector databases and retrieval-augmented generation (RAG).
• Strong understanding of MLOps, containerization (Docker), and CI/CD practices in cloud environments.
• Knowledge of data governance, privacy, and regulatory compliance in AI implementations, including GDPR and HIPAA.
• Ability to translate ambiguous business problems into scalable technical solutions through experimentation and strategic planning.