AI Engineer (Solutions Architect + Applied AI)

Role Overview

The AI Engineer (Solutions Architect + Applied AI) is responsible for designing, building, and operating the company’s AI-first technology platforms.

This role combines deep hands-on engineering with high-level architecture ownership.

You will lead the development of scalable, production-grade AI systems that power the company’s Product, and wellness technology platforms.

This is not a narrow ML experimentation role.

This role exists to ensure the company’s AI ecosystem is scalable, secure, reliable, and capable of delivering measurable real-world impact.

A major focus of the role is agentic AI development, designing and operating AI agent systems and agent-builder platforms that allow non-programmers across the company to contribute to building technology without needing to learn software engineering.

You will design the architecture, guide implementation, and ensure that the entire AI platform, from data pipelines to model deployment operates with production-grade reliability and responsible AI standards.

You will work closely with leadership, product, clinical, and engineering teams to translate product vision into real AI systems running in production.

Core Responsibilities

AI Platform Architecture and Infrastructure

• Design and maintain scalable AI-first architecture supporting multi-tenant B2B2C platforms, APIs, and white-label deployments.
• Build and maintain event-driven systems, modern data infrastructure, and distributed service architectures.
• Work extensively with Azure managed cloud services, including serverless infrastructure and containerized workloads.
• Manage infrastructure components such as Vector Databases, Feature stores, Data pipelines, CI/CD pipelines, Infrastructure-as-code, Secrets and identity management.
• Establish strong operational standards including SLIs, SLOs, error budgets, monitoring, alerting, and incident runbooks.
• Design infrastructure with cost-awareness, scalability, and reliability as primary principles.
• Leverage AI-assisted engineering workflows to accelerate architecture design, infrastructure provisioning, and system documentation.

Applied AI and Machine Learning Systems

• Translate product and clinical use cases into production AI features and model-enabled capabilities.
• Develop systems involving Retrieval-Augmented Generation (RAG), AI agents and tool-use systems, Multimodal AI applications, Time-series analysis on wearable data.
• Manage the full model lifecycle including Model evaluation frameworks, Prompt engineering and prompt versioning, Model versioning and experimentation, Offline and online A/B testing, Continuous model improvement pipelines
• Implement robust pipelines for data labeling, weak supervision, retrieval optimization, and performance monitoring.
• Maintain strong familiarity with modern AI orchestration tools including LangChain and leading LLM providers such as GPT, Claude, Gemini, and Grok.

Agentic AI and AI Driven Development

• Lead development of agentic AI systems and agent-builder platforms that enable stakeholders across the company to participate in building technology.
• Develop AI-driven workflows that support AI-assisted coding and development, Agent-driven automation pipelines, AI-assisted system configuration and infrastructure deployment.
• Use modern AI engineering approaches to accelerate build cycles, reduce manual development overhead, and improve engineering velocity.
• Contribute to building AI-enabled software development lifecycles (SDLC) including AI-assisted requirement interpretation, Automated test generation, Regression testing automation, Release validation and deployment automation.

Data Governance, Privacy, and Security

• Design systems that handle sensitive health and personal data using privacy-by-design principles.
• Define policies for PII and PHI data handling, Consent management, Data lineage and traceability, Retention policies, Cross-border data compliance
• Support integrations with external systems such as Wearables platforms, Electronic Health Records (EHR), Laboratory Information Systems (LIS), Payment systems
• Ensure all systems maintain strong security foundations including encryption, key management, and least-privilege access control.

Requirements

Required Skills & Experience

• A minimum of 8 Ai products to production with strong experience building cloud-based systems and AI-powered products.
• Minimum 4+ years experience with TypeScript using frameworks such as NestJS and Fastify.
• Minimum 4+ years experience with Python, particularly using FastAPI.
• Hands-on experience with modern AI development tools including LangGraph, LLM orchestration frameworks, Prompt engineering pipelines, Large language models including GPT, Claude, Gemini, and Grok.
• Experience working with Azure cloud infrastructure, including Azure Container Apps, Azure Functions, Azure PostgreSQL, Managed Database, Cosmos DB, Vector databases such as Qdrant.
• Demonstrated experience building AI agents or agentic workflows in production environments.
• Experience implementing AI-assisted development or code-generation workflows.
• Strong understanding of distributed systems, API design, data infrastructure, and security fundamentals.

Application Requirements

• 5-7+ years building cloud-based technology products.
• 3+ years operating as a Tech Lead, Principal Engineer, or Solutions Architect.
• Production experience deploying LLMs or AI agents at scale.
• Strong systems design capability and experience building reliable production infrastructure.

Ideal Candidate

• Strong full-stack AI engineer who is also a systems architect
• Comfortable building production-grade AI platforms
• Highly autonomous and capable of owning complex technical systems
• Passionate about agentic AI and AI-driven development workflows
• Excited about building technology that enables non-programmers to create with AI
• Thrives in fast-moving, remote, globally distributed teams

Benefits

Compensation

Monthly Retainer: USD $1,500 – $2,500

Performance Bonus: Annual bonus awarded for KPI over-performance and measurable platform impact.