IOT Technical Architect

Lennox International•Stone Mountain, GA

About The Position

Lennox is seeking a hands-on technical architect to design and deliver highly scalable, reliable, secure, and fault-tolerant enterprise systems for IOT with an AI first mindset. This role combines deep Java engineering expertise with practical architecture ownership and day-to-day leadership of technical staff. The ideal candidate can write production-quality code, guide design decisions, remove delivery blockers, and raise the engineering bar across the team. This is not a pure advisory architecture role. The expectation is strong hands-on development ownership combined with technical leadership.

Requirements

Bachelor’s, Master’s, or equivalent experience in Computer Science, Software Engineering, Information Technology, or related technical discipline with 10+ years building and operating enterprise software
Within 10+ years - 8 years of hands-on Java engineering experience with deep knowledge of JVM internals, compiler behavior, concurrency, memory management, and performance tuning
5+ years leading architecture across applications, platform, data, cloud-native services, APIs, integrations, and high-availability production platforms
Demonstrated experience designing highly scalable, reliable, secure, fault-tolerant, and production-ready enterprise systems
Strong architecture experience with microservices, APIs, event-driven architecture, integration design, DevSecOps, observability, and cloud-native engineering patterns
Experience applying AI-native development practices, AI/ML or GenAI services, automation frameworks, and intelligent system design patterns
Familiarity with multi-agent systems, skills, MCP integrations, agent-to-agent tooling.
Hands-on experience building modern full-stack and platform systems using technologies such as TypeScript/JavaScript, React, Python, Go, Java, APIs, and distributed infrastructure
Ability to leverage AI coding tools such as Claude Code, Git Hub Copilot and similar systems to improve engineering productivity and development workflows
Proven ability to lead technical teams while remaining hands-on in design, coding, code reviews, troubleshooting, and delivery execution
Strong judgment, communication, and cross-functional leadership skills, with the ability to prosper in unclear environments and stay highly hands-on through delivery

Responsibilities

Design scalable, reliable, secure, and fault-tolerant enterprise systems across distributed applications, integration, data, and cloud-native layers.
Define architecture patterns for AI-native capabilities, APIs, event-driven services, observability, resiliency, and performance at enterprise scale.
Evaluate technology options and define pragmatic technical direction across cloud, APIs, event-driven services, integrations, observability, and automation.
Spend 50%+ of time on hands-on technical work including coding, design reviews, debugging, performance tuning, AI-assisted development, and complex issue resolution.
Translate business and product needs into practical architecture decisions, technical roadmaps, and delivery-ready engineering plans.
Write production-quality code daily in Java and/or Python, while also guiding implementation across TypeScript/JavaScript, React, APIs, and distributed platform services.
Guide engineers through complex design and implementation trade-offs while ensuring solutions remain practical, supportable, and delivery ready.
Lead technical staff through architecture execution, sprint delivery, code reviews, quality gates, and production readiness activities.
Partner with product owners, business stakeholders, architects, QA, DevOps, and infrastructure teams to ensure technical delivery aligns with business outcomes.
Own technical delivery risks, remove engineering blockers, and ensure designs are implemented with measurable quality, reliability, and performance outcomes.
Design enterprise-grade integration patterns across internal systems, external platforms, APIs, data pipelines, and real-time/event-driven architectures.
Understand infrastructure layers from Kubernetes and cloud-native runtime operations to GPU inference stacks, model-serving patterns, and scalable workload deployment.
Ensure solutions are observable, secure, resilient, cost-aware, and supportable in production environments.
Establish strong practices across automated testing, CI/CD, observability, rollout safety, incident response, production support, and operational excellence.
Create reusable technical patterns, reference architectures, documentation, and engineering playbooks to standardize delivery across teams.