About the job
AI Engineer - Solutions Architect and Applied AI
Role Overview
The AI Engineer in this dual capacity of Solutions Architect and Applied AI is tasked with the critical role of conceptualizing, constructing, and managing the organization’s AI-centric technology platforms.
This position merges extensive hands-on engineering experience with overarching architectural oversight.
You will spearhead the creation of scalable, production-ready AI systems that enhance the company’s product offerings and wellness technology initiatives.
This role transcends traditional machine learning experimentation.
It is designed to ensure that the organization’s AI infrastructure is scalable, secure, dependable, and capable of generating measurable, real-world results.
A significant aspect of the position focuses on agentic AI development, which involves designing and managing AI agent systems and agent-builder platforms that empower non-programmers throughout the organization to contribute to technology development without requiring extensive software engineering knowledge.
You will architect the systems, oversee implementation, and guarantee that the complete AI platform, from data pipelines to model deployment, functions with the reliability of production-grade standards and adheres to responsible AI practices.
Collaboration with leadership, product, clinical, and engineering teams is essential to transform the product vision into operational AI systems.
Core Responsibilities
AI Platform Architecture and Infrastructure
- Design and uphold a scalable AI-first architecture that facilitates multi-tenant B2B2C platforms, APIs, and white-label implementations.
- Construct and manage event-driven systems, contemporary data infrastructures, and distributed service architectures.
- Engage extensively with Azure managed cloud solutions, including serverless architectures and containerized workloads.
- Oversee infrastructure elements like Vector Databases, Feature Stores, Data Pipelines, CI/CD Pipelines, Infrastructure-as-Code, and Secrets Management.
- Establish robust operational standards, including SLIs, SLOs, error budgets, monitoring, alerting, and incident management protocols.
- Design infrastructure while prioritizing cost-effectiveness, scalability, and reliability.
- Utilize AI-assisted engineering workflows to expedite architecture design, infrastructure provisioning, and system documentation.
Applied AI and Machine Learning Systems
- Convert product and clinical scenarios into operational AI features and model-driven capabilities.
- Develop systems that leverage Retrieval-Augmented Generation (RAG), AI agents, multimodal AI applications, and time-series analysis of wearable data.
- Manage the entire model lifecycle, including evaluation frameworks, prompt engineering and versioning, model versioning and experimentation, as well as A/B testing and continuous model improvement pipelines.
