- 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.
- 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.
- 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.
- 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.
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