Staff AI Engineer

Collective Health•Plano, TX

6d•$194,670 - $243,500•Hybrid

About The Position

At Collective Health, we’re transforming how employers and their people engage with their health benefits by seamlessly integrating cutting-edge technology, compassionate service, and world-class user experience design.

Requirements

Bachelor's degree in Computer Science, Engineering, or equivalent practical experience
10+ years of software development experience with a strong track record of delivering complex, scalable backend systems
Deep expertise in backend technologies — Java/Spring Boot and/or Python, with strong understanding of microservices architecture
Extensive hands-on experience designing and deploying cloud-native applications on AWS and/or GCP (e.g., Kubernetes, Cloud Run, App Engine, BigQuery, Cloud SQL)
Strong experience building and integrating complex RESTful and GraphQL APIs and data-intensive services
Proficiency with relational and NoSQL databases (PostgreSQL, MongoDB)
Hands-on experience with AI/ML integration in production systems, including AI-powered developer tooling
Demonstrated ability to apply data engineering principles to build reliable, high-scale data pipelines and services
Proven leadership experience — mentoring engineers, driving technical initiatives, and championing best practices
Exceptional communication skills with the ability to articulate complex technical concepts to both technical and non-technical audiences

Nice To Haves

Experience with MLOps and operationalizing AI/ML models in production
Prior experience in healthcare technology with working knowledge of HIPAA compliance
Familiarity with Google Cloud AI services (Natural Language API, Vertex AI, Gemini)
Experience with Zendesk application development and support platform integrations
Contributions to open-source projects or a strong personal project portfolio
Experience with stream processing technologies (e.g., Kafka, Spark)
Familiarity with additional cloud platforms (AWS, GCP, Azure)

Responsibilities

Set the technical vision and drive architecture, design, and development of scalable, secure, and performant backend systems and platform services
Drive architectural decisions and champion reusable, cloud-native patterns across engineering teams
Lead end-to-end development of backend services, APIs, and data pipelines that power partner integrations and downstream systems
Ensure the highest standards of code quality, security, and performance through rigorous code reviews, automated testing, and adherence to HIPAA and healthcare data compliance requirements
Proactively identify and resolve complex technical challenges, driving continuous improvement across the platform
Lead applied AI initiatives, collaborating with Engineering, Product, and Business stakeholders to integrate advanced AI models into products and services
Implement and operationalize AI/ML capabilities including large language models, intelligent search, content summarization, and automated decision workflows
Integrate and champion AI-powered developer tooling (e.g., GitHub Copilot, Cursor, Google Gemini) to accelerate engineering productivity across teams
Shape the technical roadmap for AI-driven features, ensuring responsible AI development and deployment in a healthcare context
Stay ahead of emerging AI and cloud technologies and advocate for their strategic adoption
Partner closely with Product Managers, Data Scientists, Analytics, and Operations teams to translate business requirements into technical specifications and scalable solutions
Collaborate with operations teams to improve efficiency through automation, real-time data validation, and intelligent workflows
Contribute to all phases of the software development lifecycle — from requirements and design through implementation, testing, deployment, and maintenance
Mentor and guide senior to lead engineers, fostering a culture of technical excellence, continuous learning, and collaborative problem-solving
Act as a subject matter expert and thought leader on backend development, cloud-native architecture, and applied AI for the broader engineering organization
Influence best practices in system design, testing, and continuous delivery