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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in developing and deploying AI and machine learning solutions, with a strong focus on model governance, compliance, and integration into enterprise applications. Proven ability to lead technical initiatives and collaborate across teams to drive innovation in patient care.
Highest-signal resume keywords
AI And Machine Learning Solutions DevelopmentCloud Platforms (Microsoft Azure, AWS, GCP)Model Governance And Lifecycle ManagementMLOps And CI/CD PipelinesGenerative AI Applications Development
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Machine Learning ModelsPredictive Analytics SolutionsData PipelinesNatural Language ProcessingContainerization TechnologiesModel DeploymentAI GovernanceAuditability And Fail-Closed ChecksPerformance AssessmentTechnical Documentation
Soft Skills
Analytical SkillsProblem-Solving SkillsCollaborationTeamworkProject Management
Tools & Technologies
AI PlatformsCloud-Native AI ServicesDistributed ComputingIntelligent Automation ToolsArchitecture Diagrams
Industry Keywords
HealthcareClinical ResearchData PrivacyCybersecurityResponsible AI Practices
Tech Stack
Tools & technologiesAWSAzureCloudCyber SecurityGoogle Cloud Platform
About the role
Key responsibilities & impact- Define and drive Sharecare's transformation into the era of agentic engineering
- Develop and improve the Factory spine to maximize agentic engineering throughput
- Evolve factory platform capabilities including merge gates, migration safety, canary deployment, and rollback
- Own the application work spine enabling AI agents and humans to act on patient care with auditability and fail-closed checks
- Own the warehouse, analytics platform, data pipelines, and metrics
- Design, develop, test, deploy, and maintain AI and machine learning solutions
- 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
- Integrate AI and machine learning models into enterprise applications, workflows, and business processes
- Develop and maintain data pipelines for model training, evaluation, deployment, and monitoring
- Evaluate emerging AI technologies, frameworks, and platforms
- Monitor model performance and implement retraining, optimization, and lifecycle management
- Ensure AI solutions meet security, privacy, compliance, and governance standards
- Conduct testing, validation, and performance assessments of models and AI applications
- Collaborate with cloud engineering and infrastructure teams to deploy and scale AI solutions
- Develop technical documentation, architecture diagrams, and operational procedures
- Support AI governance initiatives involving transparency, explainability, fairness, and responsible AI
- Troubleshoot technical issues related to AI models, integrations, and production environments
- Participate in code reviews, solution design sessions, and architectural planning
- Provide technical leadership on AI best practices and implementation strategies
- Contribute to the organization's AI roadmap and strategic innovation initiatives
Requirements
What you’ll need- Bachelor's degree in Computer Science, Artificial Intelligence, Data Science, Software Engineering, Mathematics, Statistics, Information Technology, or a related field required
- Minimum of 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 (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
- Knowledge of data privacy, cybersecurity, compliance, and responsible AI practices
- Experience developing solutions using LLMs, generative AI platforms, and natural language processing tools preferred
- Experience with model deployment, MLOps, DevOps, CI/CD pipelines, and containerization technologies preferred
- Prior healthcare and/or clinical research experience is a plus
- 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
- Ability to manage multiple priorities and projects in a fast-paced environment
- High level of professionalism, integrity, and accountability