What "a fit" looks like, concretely
Demonstrate you can ship daily at high volume with a fleet of AI agents. In the interview we'll ask to see your actual setup and what you let it do unsupervised. "I use Copilot sometimes" is not this.
You've owned architecture across multiple companies or many codebases: staff-plus IC, chief-architect, or platform-consulting background. You've seen a dozen ways teams ship and you have opinions with scars behind them.
When handed an ambiguous, ugly, load-bearing problem, your instinct is to take it whole: read everything, decide, build, write the doc, tell us what you decided. Not to schedule a meeting about scope.
Prior experience working in healthcare and/or clinical research, on health data products including electronic health records, claims, wearables, etc. and/or remote patient monitoring technologies is a plus
You communicate proactively in writing. For example: a Sunday text that says "took care of it, here's the PR and what I changed."
6+ years and multiple systems owned end to end
What you will own
The Factory spine. Develop and improve our existing factory to maximize and align in the era of agentic engineering. Increase our throughput by 50% while maintaining a lean team. Your job is to evolve our factory platform: Merge gates, migration safety, canary and rollback on a system where a bad deploy can touch patient care.
The application work spine. The layer that lets AI agents and humans act on patient care with auditability and fail-closed checks. You’ll own the guardrails that ensure agents can safely provide broader components of care.
The data and metrics. Warehouse, analytics platform, and the pipelines that tell us what happened.
Architecture syncs with me. You'll have the standing I give someone who has seen more systems than I have: disagree with me, make the call in your domain, unpack it for the team when asked.
Essential Job Functions
Design, develop, test, deploy, and maintain AI and machine learning solutions to support business needs.
Build and optimize machine learning models, predictive analytics solutions, recommendation systems, and intelligent automation tools.
Develop and implement generative AI applications utilizing large language models (LLMs), retrieval-augmented generation (RAG), and conversational AI technologies.
Collaborate with business stakeholders to understand requirements and translate business challenges into AI-driven solutions.
Integrate AI and machine learning models into enterprise applications, workflows, and business processes.
Develop and maintain data pipelines that support model training, evaluation, deployment, and ongoing monitoring.
Evaluate emerging AI technologies, frameworks, and platforms to identify opportunities for innovation and business improvement.
Monitor model performance and implement processes for model retraining, optimization, and lifecycle management.
Ensure AI solutions meet security, privacy, compliance, and governance standards.
Conduct testing, validation, and performance assessments of machine learning models and AI applications.
Collaborate with cloud engineering and infrastructure teams to deploy and scale AI solutions in cloud environments.
Develop technical documentation, architecture diagrams, and operational procedures for AI systems.
Support AI governance initiatives, including model transparency, explainability, fairness, and responsible AI practices.
Troubleshoot and resolve technical issues related to AI models, integrations, and production environments.
Participate in code reviews, solution design sessions, and architectural planning activities.
Provide technical leadership and guidance on AI best practices and implementation strategies.
Stay current with advancements in artificial intelligence, machine learning, data science, and related technologies.
Support strategic innovation initiatives and contribute to the organization's AI roadmap.
Qualifications
Education
Bachelor's degree in Computer Science, Artificial Intelligence, Data Science, Software Engineering, Mathematics, Statistics, Information Technology, or a related field required.
Experience
Minimum of at least 4 years of experience software engineering or systems architecture.
Experience developing, deploying, and supporting machine learning models and AI-driven applications.
Experience developing solutions using large language models (LLMs), generative AI platforms, and natural language processing tools preferred.
Experience with cloud platforms such as Microsoft Azure, Amazon Web Services (AWS), or Google Cloud Platform (GCP).
Experience working with model deployment, MLOps, DevOps, CI/CD pipelines, and containerization technologies preferred.
Knowledge, Skills, and Abilities
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 with the ability to work effectively 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.
High level of professionalism, integrity, and accountability.
Sharecare and its subsidiaries are Equal Opportunity Employers and E-Verify users. Qualified applicants will receive consideration for employment without regard to race, color, sex, national origin, sexual orientation, gender identity, religion, age, equal pay, disability, genetic information, protected veteran status, or other status protected under applicable law.
