AI Engineer

Sharecare•Atlanta, GA

About The Position

Sharecare is a digital healthcare company offering software and technology-enabled services across the healthcare ecosystem. The AI Engineer will help define and drive Sharecare’s transformation into agentic engineering, taking ownership of complex, ambiguous problems and delivering working, documented solutions with substantial independence. The role calls for an engineer who can use a fleet of AI agents to ship frequently, has owned architecture across multiple companies or codebases, and can make and communicate decisions without relying on narrowly scoped tickets or frequent oversight. The position owns three areas: the Factory spine, including the platform and practices that support agentic engineering; the application work spine, which enables safe, auditable AI-agent and human interactions with patient care; and the data and metrics layer, including the warehouse, analytics platform, and pipelines. The engineer will evolve factory capabilities such as merge gates, migration safety, canary deployments, and rollback, while helping increase throughput by 50% and maintaining a lean team. They will also contribute to architecture decisions and explain those decisions to the team. In addition, the AI Engineer designs, builds, deploys, monitors, and maintains AI and machine learning solutions, including generative AI applications using LLMs, RAG, and conversational AI. The role works with business stakeholders and technical teams to integrate AI into enterprise applications and processes, develop supporting data pipelines, meet security and compliance standards, and advance responsible AI practices. Healthcare or clinical research experience is a plus.

Requirements

Bachelor’s degree in Computer Science, Artificial Intelligence, Data Science, Software Engineering, Mathematics, Statistics, Information Technology, or a related field.
At least 4 years of experience in software engineering or systems architecture.
Experience developing, deploying, and supporting machine learning models and AI-driven applications.
Experience with cloud platforms such as Microsoft Azure, Amazon Web Services, or Google Cloud Platform.
Familiarity with model governance, monitoring, and lifecycle management.
Knowledge of cloud-native AI services, distributed computing, and scalable architecture principles.
Strong analytical, problem-solving, and critical-thinking skills.
Ability to translate complex technical concepts into business-focused solutions and recommendations.
Strong collaboration and teamwork skills across technical and business functions.
Ability to manage multiple priorities and projects in a fast-paced environment.
Knowledge of data privacy, cybersecurity, compliance, and responsible AI practices.
Professionalism, integrity, and accountability.
The role description calls for 6+ years and ownership of multiple systems end to end; the qualifications section separately specifies a minimum of 4 years of software engineering or systems architecture experience.
Ability to take ownership of ambiguous, complex problems and deliver working, documented solutions independently.
Experience shipping at high volume using a fleet of AI agents.

Nice To Haves

Experience developing solutions with large language models, generative AI platforms, and natural language processing tools.
Experience with model deployment, MLOps, DevOps, CI/CD pipelines, and containerization technologies.
Prior experience in healthcare or clinical research, including health data products such as electronic health records, claims, or wearables, or remote patient monitoring technologies.
Staff-plus individual contributor, chief architect, or platform consulting background, with architecture ownership across multiple companies or many codebases.

Responsibilities

Develop and improve Sharecare’s factory platform for agentic engineering, including merge gates, migration safety, canary deployments, and rollback.
Evolve the application work spine so AI agents and people can act on patient care with auditability and fail-closed checks.
Own the warehouse, analytics platform, and data pipelines used to understand system and business outcomes.
Design, develop, test, deploy, and maintain AI and machine learning solutions for business needs.
Build and optimize machine learning models, predictive analytics, recommendation systems, and intelligent automation tools.
Develop generative AI applications using large language models, retrieval-augmented generation, and conversational AI technologies.
Collaborate with business stakeholders to translate requirements and challenges into AI-driven solutions.
Integrate AI and machine learning models into enterprise applications, workflows, and business processes.
Develop and maintain data pipelines for model training, evaluation, deployment, and ongoing monitoring.
Evaluate emerging AI technologies, frameworks, and platforms for opportunities to improve and innovate.
Monitor model performance and implement retraining, optimization, and lifecycle-management processes.
Ensure AI solutions meet security, privacy, compliance, and governance standards.
Test, validate, and assess the performance of machine learning models and AI applications.
Collaborate with cloud engineering and infrastructure teams to deploy and scale AI solutions in cloud environments.
Create technical documentation, architecture diagrams, and operational procedures for AI systems.
Support AI governance, transparency, explainability, fairness, and responsible AI practices.
Troubleshoot and resolve issues involving AI models, integrations, and production environments.
Participate in code reviews, solution design sessions, and architectural planning.
Provide technical leadership and guidance on AI best practices and implementation strategies.
Stay current with advancements in AI, machine learning, data science, and related technologies.
Support strategic innovation initiatives and contribute to the organization’s AI roadmap.
Communicate proactively in writing and make and explain architecture decisions in the role’s domain.