AI Solution Architect

8-10 years exp. Solution Architect

We are seeking an experienced Solution Architect – Cloud, IoT & AI-Driven Platforms to lead the architectural design and modernization of an enterprise-scale remote instrument monitoring platform within the life sciences and clinical diagnostics domain.

The platform enables remote monitoring of diagnostic instruments deployed across customer environments, collecting real-time telemetry and providing visibility into instrument health, operational status, diagnostics, and software updates. The initiative involves transforming a legacy application landscape into a scalable, secure, cloud-native architecture that leverages IoT connectivity, event-driven processing, advanced analytics, and AI-powered multi-agent capabilities.

The Solution Architect will define the end-to-end target architecture across three interconnected layers: Application (Experience), Transport, and Processing. The role will establish architectural direction, technical standards, integration contracts, security frameworks, and AI governance while leading and guiding three distributed engineering teams across the US, India, and Israel.

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.

• 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.

• 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.

• 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.

• 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 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.

• 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