AI/ML Architect

Years of Experience-5-14 years

• Bachelors degree in Computer Science, Engineering, Data Science, or related field
• 5+ years of hands-on software engineering experience in deploying distributed systems and cloud-native solutions
• 5+ years of experience operating in a consulting or advisory capacity, including structuring ambiguous problems, developing solution recommendations, and delivering against client or business objectives
• Experience working in client-facing engagements, including partnering directly with stakeholders to understand needs, communicate solutions, and support delivery in real-world environments
• Experience building and deploying AI/ML solutions, including familiarity with LLMs, prompt engineering, and model deployment patterns
• Experience structuring ambiguous problems and driving them to clear, actionable solutions
• Experience working in cloud environments (e.g., Azure, AWS, GCP)
• Proficiency in modern development frameworks, APIs, and data integration approaches
• Advanced user capability on CodeX, Claude, etc. and other AI productivity tools
• Proven ability to lead client engagements, including discovery, solution design, and delivery
• Proven solid stakeholder management skills, with the ability to influence both technical and executive audiences
• Proven excellent communication and storytelling skills, with the ability to adapt messaging to different audiences

Preferred Qualifications:

• Masters degree in a technical discipline (AI/ML, Data Science, Software Engineering)
• Healthcare Domain Expertise; understanding of healthcare payer and/or provider operations (e.g., claims, utilization management, care delivery, revenue cycle)
• Familiarity with healthcare data, regulatory requirements, and interoperability considerations
• Proven to be comfortable operating in fast-paced, ambiguous environments with evolving client needs
• Proven solid collaboration mindset with the ability to work across distributed and cross-functional teams
• Proven entrepreneurial, solution-oriented approach with a bias toward action and outcomes