AI Engineer (Solutions Architect + Applied AI)

• 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.
• 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.
• 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.

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.