AI Engineer- Remote

Sharecare•Atlanta, GA

1d•Remote

About The Position

Sharecare is seeking an AI Engineer to contribute across vision, strategy, operations, and execution as the company transforms toward agentic engineering. The role is intended for an engineer who can independently take ownership of broad, ambiguous technical problems and deliver working, documented solutions. The posting describes a strong fit as someone who ships frequently using a fleet of AI agents, has owned architecture across multiple companies or codebases, and can make and communicate decisions without waiting for tightly scoped tickets. Six or more years of experience and ownership of multiple systems end to end are cited in this fit description; the qualifications section separately lists a minimum of four years of software engineering or systems architecture experience. The engineer will own and evolve the engineering factory platform, with goals including increasing throughput by 50% while maintaining a lean team. This includes merge gates, migration safety, canary releases, and rollback mechanisms for systems where deployments can affect patient care. The role will also own the application-work layer that enables AI agents and people to act on patient care with auditability and fail-closed checks, as well as the warehouse, analytics platform, and data pipelines used to measure outcomes. The engineer will participate in architecture syncs and make decisions within their domain. Essential duties include designing, developing, testing, deploying, maintaining, and monitoring AI and machine-learning solutions; building models, predictive analytics, recommendation systems, intelligent automation, and generative AI applications using LLMs, RAG, and conversational AI; integrating these capabilities into enterprise applications and workflows; and developing data pipelines for model training, evaluation, deployment, and monitoring. The role also involves evaluating emerging technologies, managing model performance and lifecycle, ensuring security, privacy, compliance, and governance, conducting model and application testing, collaborating with cloud and infrastructure teams, creating technical documentation and operational procedures, supporting responsible AI practices, troubleshooting production issues, participating in reviews and architecture planning, providing technical leadership, and contributing to the AI roadmap. A bachelor's degree in a related discipline is required. The qualifications list at least four years of software engineering or systems architecture experience, experience developing, deploying, and supporting machine-learning models and AI applications, and experience with cloud platforms. Experience with LLMs, generative AI, natural language processing, MLOps, DevOps, CI/CD, and containerization is preferred. Prior healthcare or clinical research experience, health data product experience, or remote patient monitoring experience is described as 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 four years of software engineering or systems architecture experience.
Experience developing, deploying, and supporting machine-learning models and AI-driven applications.
Experience with cloud platforms such as Microsoft Azure, Amazon Web Services (AWS), or Google Cloud Platform (GCP).
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-fit description cites six or more years of experience and ownership of multiple systems end to end.
Ability to independently take ownership of ambiguous, broad technical problems and deliver working, documented solutions.
Ability to communicate proactively in writing.

Nice To Haves

Experience developing solutions using large language models (LLMs), generative AI platforms, and natural language processing tools.
Experience with model deployment, MLOps, DevOps, CI/CD pipelines, and containerization technologies.
Prior experience in healthcare and/or clinical research, health data products such as electronic health records, claims, or wearables, and/or remote patient monitoring technologies.
Experience shipping frequently with a fleet of AI agents and owning architecture across multiple companies or many codebases.

Responsibilities

Develop and improve Sharecare’s engineering factory platform for agentic engineering, including merge gates, migration safety, canary releases, and rollback mechanisms.
Work toward increasing engineering throughput by 50% while maintaining a lean team.
Own the application-work layer that enables AI agents and humans to act on patient care with auditability and fail-closed checks.
Own the warehouse, analytics platform, and data pipelines that report what happened.
Participate in architecture syncs, make decisions within the role’s domain, and explain decisions to the team when requested.
Design, develop, test, deploy, and maintain AI and machine-learning solutions for business needs.
Build and optimize machine-learning models, predictive analytics solutions, recommendation systems, and intelligent automation tools.
Develop generative AI applications using LLMs, RAG, and conversational AI technologies.
Collaborate with business stakeholders to understand requirements and turn business 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 innovation and business improvement.
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 model transparency, explainability, fairness, and responsible AI governance initiatives.
Troubleshoot and resolve technical problems 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 developments in AI, machine learning, data science, and related technologies.
Support strategic innovation initiatives and contribute to the organization’s AI roadmap.