Define and own the end-to-end target architecture and technical roadmap for the remote instrument monitoring platform.

Design scalable, modular, cloud-native architectures across Application, Transport, and Processing layers.

Establish architectural principles, integration patterns, interface contracts, versioning strategies, and ownership boundaries.

Ensure the platform supports additional applications and AI agents without significant architectural redesign.

Evaluate technology choices, build-versus-buy decisions, architectural trade-offs, and modernization strategies.

Maintain architectural consistency through design reviews, technical guidance, and architecture decision records.

Own the architecture of the IoT Transport layer, supporting device telemetry, heartbeat monitoring, status updates, events, remote commands, and large-file transfers.

Design reliable device connectivity solutions for customer firewalls, TLS-inspecting proxies, restricted networks, and intermittent connectivity environments.

Define secure communication patterns and APIs for instrument-to-cloud and application-to-platform interactions.

Architect the Model Context Protocol (MCP) layer to expose instrument capabilities as governed, permission-controlled, and auditable tools for AI agents.

Establish scalable cloud infrastructure and integration patterns, preferably using AWS services and cloud-native technologies.

Define device onboarding, identity management, authentication, authorization, and secure communication standards.

Own the Processing layer architecture, including data ingestion, ETL pipelines, operational data stores, analytics platforms, and event-driven processing.

Design scalable data processing solutions for large instrument fleets, high-volume telemetry, and variable workloads.

Define multi-agent AI architectures with orchestrators and specialized agents using Agent-to-Agent (A2A) communication standards.

Architect LLM-based solutions for instrument diagnostics, predictive maintenance, anomaly detection, operational intelligence, and service recommendations.

Establish integration patterns using MCP, A2A, APIs, and event streams.

Define AI agent evaluation frameworks, Retrieval-Augmented Generation (RAG), guardrails, prompt management, and model performance monitoring.

Ensure human approval and authorization mechanisms before AI agents execute actions affecting instruments or customer records.

Establish AI governance frameworks covering advisory versus actionable outputs, human-in-the-loop controls, model validation, and traceability.

Define standards for model and prompt versioning, evaluation, regression testing, and controlled deployment.

Architect secure data handling practices addressing privacy, data residency, confidentiality, and access controls.

Define end-to-end security architecture covering device identity, hardware-backed keys, token management, encryption, signed software delivery, and least-privilege access.

Establish threat models and security controls across Application, Transport, Processing, and Device/Edge layers.

Ensure architecture supports regulated product development, audit readiness, traceability, and controlled releases.

Incorporate applicable compliance requirements, including FDA 21 CFR Part 11, ISO 13485, IEC 62304, and relevant medical-device cybersecurity standards.

Lead the architectural strategy for migrating legacy applications and services to a modern cloud-native platform.

Assess legacy systems, including .NET Framework, ASP.NET, VB6/COM, and SQL Server-based applications, to identify critical business functionality and dependencies.

Define coexistence and migration strategies, including dual-running, data parity verification, migration waves, rollback procedures, and legacy decommissioning.

Ensure uninterrupted monitoring and support of the existing instrument fleet throughout modernization.

Design offline-tolerant data synchronization and software delivery mechanisms for connected, intermittently connected, and air-gapped customer environments.

Define and validate non-functional requirements covering scalability, performance, availability, resilience, security, and disaster recovery.

Establish architecture standards for telemetry latency, high-volume ingestion, burst handling, multi-GB file transfers, resumable uploads, and data integrity.

Define observability, monitoring, logging, alerting, and operational support strategies.

Establish cloud cost optimization principles and cost-per-instrument metrics.

Define infrastructure automation, environment management, deployment pipelines, and controlled release processes.

Strong experience in enterprise architecture and end-to-end solution architecture.

Expertise in AWS cloud services and cloud-native architecture.

Experience designing IoT platforms, device connectivity, telemetry, and secure communication solutions.

Knowledge of Model Context Protocol (MCP) and Agent-to-Agent (A2A) architecture.

Experience with Generative AI, LLMs, AI agents, RAG, and AI orchestration.

Strong understanding of data engineering, ETL pipelines, event-driven processing, and analytics platforms.

Expertise in cloud security, identity management, authentication, authorization, encryption, and threat modeling.

Experience with legacy application modernization and cloud migration.

Knowledge of .NET Framework, ASP.NET, VB6/COM, and Microsoft SQL Server.

Understanding of regulatory and compliance standards, including FDA 21 CFR Part 11, ISO 13485, and IEC 62304.

Strong knowledge of scalability, reliability, performance optimization, disaster recovery, and observability.

Excellent technical leadership, communication, and stakeholder management skills.