Senior AI Solutions Engineer

About the role

The Senior AI Solutions Engineer (Engineer Senior) will build and scale intelligent systems that improve healthcare outcomes. This role requires associates to be in-office 1-2 days per week, fostering collaboration and connectivity, while providing flexibility to support productivity and work-life balance.

Responsibilities

• Maintains active relationships with product, data, engineering, architecture, and business teams to translate business problems into scalable AI/ML solutions.
• Applies AI/ML capabilities and modern AI tools to solve meaningful business problems, improve healthcare outcomes, and enable practical adoption across business workflows, digital products, and enterprise applications.
• Owes the change request process and may coordinate with other teams as necessary.
• Provides technical advice and weighs in on technical decisions that impact cross-functional teams, including AI/ML and advanced analytics solutions.
• Researches and may propose new technologies, keeping abreast of developments in AI and applying them to enterprise use cases where relevant.
• Designs, builds, and deploys end-to-end ML and LLM-based systems (data pipelines, models, prompts, APIs, and monitoring).
• Develops, evaluates, deploys, and monitors machine learning models for predictive, classification, optimization, personalization, and decision-support use cases.
• Builds applied AI solutions such as semantic search, RAG pipelines, and embedding-based systems.
• Develops production-ready AI/ML services, APIs, pipelines, and reusable components; improves performance, scalability, reliability, usability, and operational readiness in production.
• Performs technical design reviews and code reviews; provides technical leadership through solution design, best practices, troubleshooting, and mentoring junior team members.
• Affirms status and supports data information planning.
• Coordinates on-call support and ensures effective monitoring of systems, including AI/ML model performance and reliability.
• Mentors others and may lead multiple or small to medium-sized projects; influences engineering standards and technical direction.
• Identifies and proposes strategies around technical problems affecting the team, communicates standards, and gains buy-in on solutions.

Requirements

• Requires a BA/BS degree in Information Technology, Computer Science or related field of study and a minimum of 5 years related experience; multi-dimensional platform experience; experience with business and technical applications, or any combination of education and experience, which would provide an equivalent background.

Preferred Skills, Capabilities, And Experiences

• Masters or PhD preferred.
• Advanced Python skills for machine learning and data processing preferred.
• Experience across the full ML lifecycle (development → deployment → monitoring) preferred.
• Hands-on experience with LLMs, embeddings, RAG, or modern NLP techniques using vector search technologies like OpenSearch preferred.
• Experience building production AI systems (APIs, pipelines, microservices) preferred.
• Experience designing evaluation frameworks for ML/LLM systems preferred.
• Familiarity with responsible AI principles (fairness, explainability, privacy) preferred.
• Experience with cloud-based ML platforms and scalable architectures (AWS Bedrock, or equivalent AI model cloud providers) preferred.
• Healthcare domain experience, including familiarity with data privacy and regulatory requirements preferred.