AI Engineer (Solutions Architect + Applied AI)

Role OverviewThe 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 ResponsibilitiesAI Platform Architecture and InfrastructureDesign and maintain scalable AI-first architecture supporting multi-tenant B2B2C platforms, APIs, and white-label deploymentsBuild and maintain event-driven systems, modern data infrastructure, and distributed service architecturesWork extensively with Azure managed cloud services, including serverless infrastructure and containerized workloadsManage infrastructure components such as Vector Databases, Feature stores, Data pipelines, CI/CD pipelines, Infrastructure-as-code, Secrets and identity managementEstablish strong operational standards including SLIs, SLOs, error budgets, monitoring, alerting, and incident runbooksDesign infrastructure with cost-awareness, scalability, and reliability as primary principlesLeverage AI-assisted engineering workflows to accelerate architecture design, infrastructure provisioning, and system documentationApplied AI and Machine Learning SystemsTranslate product and clinical use cases into production AI features and model-enabled capabilitiesDevelop systems involving Retrieval-Augmented Generation (RAG), AI agents and tool-use systems, Multimodal AI applications, Time-series analysis on wearable dataManage 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 pipelinesImplement robust pipelines for data labeling, weak supervision, retrieval optimization, and performance monitoringMaintain strong familiarity with modern AI orchestration tools including LangChain and leading LLM providers such as GPT, Claude, Gemini, and GrokAgentic AI and AI Driven DevelopmentLead development of agentic AI systems and agent-builder platforms that enable stakeholders across the company to participate in building technologyDevelop AI-driven workflows that support AI-assisted coding and development, Agent-driven automation pipelines, AI-assisted system configuration and infrastructure deploymentUse modern AI engineering approaches to accelerate build cycles, reduce manual development overhead, and improve engineering velocityContribute to building AI-enabled software development lifecycles (SDLC) including AI-assisted requirement interpretation, Automated test generation, Regression testing automation, Release validation and deployment automationData Governance, Privacy, and SecurityDesign systems that handle sensitive health and personal data using privacy-by-design principlesDefine policies for PII and PHI data handling, Consent management, Data lineage and traceability, Retention policies, Cross-border data complianceSupport integrations with external systems such as Wearables platforms, Electronic Health Records (EHR), Laboratory Information Systems (LIS), Payment systemsEnsure all systems maintain strong security foundations including encryption, key management, and least-privilege access controlsRequirementsRequired Skills & ExperienceStrong experience building cloud-based systems and AI-powered productsMinimum 4+ years experience with TypeScript using frameworks such as NextJS and FastifyMinimum 4+ years experience with Python, particularly using FastAPIHands-on experience with modern AI development tools including LangChain, LLM orchestration frameworks, Prompt engineering pipelines, Large language models including GPT, Claude, Gemini, and GrokExperience working with Azure cloud infrastructure, including Azure Container Apps, Azure Functions, Azure PostgreSQL, Managed Database, Cosmos DB, Vector databases such as QdrantDemonstrated experience building AI agents or agentic workflows in production environmentsExperience implementing AI-assisted development or code-generation workflowsStrong understanding of distributed systems, API design, data infrastructure, and security fundamentalsApplication Requirements8-12+ years building cloud-based technology products3+ years operating as a Tech Lead, Principal Engineer, or Solutions ArchitectProduction experience deploying LLMs or AI agents at scaleStrong systems design capability and experience building reliable production infrastructureIdeal CandidateStrong full-stack AI engineer who is also a systems architectComfortable building production-grade AI platformsHighly autonomous and capable of owning complex technical systemsPassionate about agentic AI and AI-driven development workflowsExcited about building technology that enables non-programmers to create with AIThrives in fast-moving, remote, globally distributed teamsBenefitsCompensationMonthly Retainer: USD $1,500 - $2,500Performance Bonus: Annual bonus awarded for KPI over-performance and measurable platform impact.