Solution Architect – Cloud, IoT & AI-Driven Platforms

Job Description

This is a hands-on architectural leadership position requiring strong expertise in cloud modernization, distributed systems, IoT architecture, enterprise integration, and production-grade AI/LLM-based agent solutions.

Key Responsibilities

• Enterprise & Solution Architecture
• Define and own the end-to-end target architecture and technical roadmap for the next-generation remote instrument monitoring platform.
• Design a scalable, modular, cloud-native architecture comprising Application, Transport, and Processing layers.
• Establish architectural principles, integration patterns, interface contracts, versioning strategies, and ownership boundaries across engineering teams.
• Ensure the platform supports onboarding additional applications and AI agents without requiring significant architectural redesign.
• Evaluate technology choices, build-versus-buy decisions, architectural trade-offs, and modernization strategies.
• Maintain architectural consistency across teams through design reviews, technical guidance, and architecture decision records.
• Cloud Platform & IoT Architecture
• 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 that operate across 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 and operational functions as governed, permission-controlled, and auditable tools for AI agents.
• Establish scalable cloud infrastructure and integration patterns, preferably leveraging AWS services and cloud-native technologies.
• Define device onboarding, identity management, authentication, authorization, and secure device communication standards.
• Data Engineering & AI Agent Architecture
• Own the Processing layer architecture, including data ingestion, ETL pipelines, operational data stores, analytics platforms, and event-driven processing.
• Design scalable data processing solutions capable of handling large instrument fleets, high-volume telemetry, and variable workloads.
• Define a multi-agent AI architecture comprising an orchestrator and specialized agents that collaborate using Agent-to-Agent (A2A) communication standards.
• Architect LLM-based capabilities for instrument diagnostics, predictive maintenance, anomaly detection, operational intelligence, and service recommendations.
• Establish integration patterns using MCP, A2A, APIs, event streams, and other relevant interoperability frameworks.
• Define AI agent evaluation frameworks, retrieval-augmented generation (RAG), guardrails, prompt management, and model performance monitoring.
• Ensure human approval and authorization mechanisms are incorporated before AI agents execute actions affecting instruments or customer records.
• AI Governance, Security & Regulatory Compliance
• 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.
• Legacy Modernization & Platform Migration
• Lead the architectural strategy for migrating legacy applications and services to a modern cloud-native platform.
• Assess existing 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 the modernization lifecycle.
• Design offline-tolerant data synchronization and software delivery mechanisms for connected, intermittently connected, and air-gapped customer environments.
• Non-Functional Architecture & Operational Excellence
• 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 rele