We are seeking a
Senior Platform Engineer, Delivery & Reliability to own the trusted software delivery and reliability platform that enables product teams to move from idea to customer impact with speed, safety, and confidence.
RDI Technologies is a Knoxville, Tennessee-based technology company that invented an entirely new category of industrial sensing. Founded on the idea that the camera is the sensor of the future, RDI's proprietary Motion Amplification® platform enables us to see and measure motion that is invisible to the human eye, turning every pixel into a sensor. What started in 2013 as a university lab spin-out has become one of the nation's fastest-growing private companies, earning a spot on the Inc. 5000 list for three consecutive years. Today, RDI's technology is used by over 650 corporate clients across 38+ industries in more than 62 countries, serving sectors from aerospace and defense to oil and gas, power generation, and automotive. The company's Iris product family is redefining how the world's most critical infrastructure is maintained and how products are designed and tested.
This role is not simply responsible for writing CI/CD pipelines, GitHub Actions workflows, or build scripts. As AI increasingly generates code, tests, automation, documentation, and autonomous agents that execute delivery tasks, the durable value of this role is owning the agentic system of trust around software delivery: what must be proven, automated, observed, governed, and recoverable before software is built, packaged, released, installed, updated, or operated in the field.
This is not a narrow automation role. It is a platform ownership role at the center of engineering scale. You will have influence across the full delivery lifecycle: from code to validated artifact, from release candidate to customer installation, and from operational incident back into stronger automation, better release gates, and more reliable products.
The Senior Platform Engineer turns the software delivery lifecycle into a reliable internal product. They create the paved roads, standards, automation, observability, and guardrails that allow human engineers and AI-assisted workflows to ship better software with less friction and lower risk.
For the right person, this role offers a rare combination of technical depth, cross-functional influence, platform ownership, reliability impact, and future-facing AI governance. You will help build the foundation that allows teams to move faster without becoming fragile, automate more without losing control, and release more confidently across software, hardware, and field-deployed products.
This role is based in our Knoxville HQ to facilitate collaboration across our software and hardware teams, and no sponsorship will be provided for this role.
The mission of this role is to improve the business outcomes produced by engineering by making software delivery faster, safer, more observable, more repeatable, and more resilient. This role exists to answer questions such as:
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How quickly can a product team move from code change to validated release artifact?
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Can we prove what changed, what was tested, what was approved, and what was released?
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Can we consistently build, package, sign, install, update, and recover our software?
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Can engineers and AI-assisted workflows safely use the same trusted delivery paths?
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Can we detect failures early, respond quickly, and prevent the same issues from recurring?
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Can our delivery platform scale across products, hardware environments, test systems, and field deployments?
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Can engineers, AI-assisted workflows, and autonomous AI agents safely use the same trusted delivery paths?
The role owns the platform and practices that make those outcomes possible.
Treat the delivery and reliability platform as a product used by software, hardware, QA, application engineering, support, and AI-assisted workflows.
Define the roadmap, standards, paved roads, and platform patterns that reduce engineering friction, release risk, and operational failure.
Own the workflows that move changes from source control through build, test, artifact generation, signing, packaging, release approval, deployment, installation, update, and monitoring.
Ensure delivery paths are automated, traceable, reproducible, governed, and easier to use than risky manual alternatives.
Partner with engineering and QA to define what must be true before software is considered releasable.
Build release gates, dashboards, and validation workflows that provide evidence of build health, test results, artifact readiness, release risk, installer/update quality, and deployment status.
Establish guardrails for AI-generated code, tests, scripts, workflow changes, documentation, release preparation, and autonomous delivery agents.
Define the approval, audit, rollback, policy-as-code, and human-in-the-loop controls required for AI to accelerate delivery without increasing downstream risk.
Connect build, test, release, deployment, observability, and incident response into one feedback loop.
Use operational signals, incidents, health checks, alerts, SLOs, error budgets, and post-incident findings to improve release gates, recovery paths, automation, and system reliability.
Build delivery automation that supports software, firmware, hardware builds, device updates, installers, test rigs, and field-deployed systems.
Reduce fragile handoffs between software, hardware, QA, application engineering, and field support so what is built, tested, packaged, shipped, and installed is correct and supportable.
Product teams can move from source control to validated release artifacts with fewer manual handoffs, faster feedback loops, and more self-service delivery paths.
Measured by: lead time, build reliability, test feedback speed, artifact promotion flow, and developer self-service adoption.
Every release has clear evidence that it is ready to ship, including what changed, what was tested, what was approved, and what artifact is being released.
Measured by: traceability, release gate quality, reproducible builds, installer/update validation, version accuracy, and visible release risk.
Human engineers, AI-assisted workflows, and autonomous agents can use the same trusted delivery paths without increasing downstream risk.
Measured by: guardrails for generated code and automation, approval workflows, secrets and permission controls, policy-as-code, auditability, rollback readiness, and human-in-the-loop escalation for high-risk actions.
Delivery workflows support real product constraints across software, firmware, hardware builds, installers, test rigs, device updates, and field-deployed systems.
Measured by: repeatable handoffs, hardware-in-the-loop validation, compatibility checks, update manifests, deployment metadata, and reliable customer installation/update workflows.
Build, release, deployment, and field operations are connected so failures are detected earlier, recovered from faster, and prevented from recurring.
Measured by: uptime practices, observability, health checks, incident readiness, mean time to detect, mean time to recover, root-cause follow-through, SLOs, and error budgets.
Engineers spend less time fighting pipelines, environments, packaging, release steps, and tribal knowledge, and more time building product value.
Measured by: reusable workflows, internal templates, self-service automation, usable documentation, reduced duplication, and clear paved roads for build-test-package-release-install-update workflows.
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5+ years of professional experience in platform engineering, software engineering, DevOps, release engineering, SRE, infrastructure automation, build engineering, or related roles.
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Strong experience designing and operating CI/CD systems, preferably using GitHub Actions.
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Experience building or maintaining delivery automation across build, test, artifact generation, packaging, release, deployment, or installation workflows.
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Strong scripting or programming experience with tools such as PowerShell, Bash, Python, C#, JavaScript/TypeScript, or similar.
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Experience with source control workflows, pull requests, branch protection, repository standards, and release controls.
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Experience improving developer productivity through reusable workflows, templates, automation, documentation, or self-service tooling.
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Understanding of automated testing and how quality signals fit into release pipelines.
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Familiarity with observability, monitoring, alerting, incident response, and operational reliability practices.
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Ability to work across engineering, QA, hardware, application, support, and product teams.
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Strong written communication skills for standards, runbooks, technical proposals, and platform documentation.
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Experience with hardware/software products, embedded systems, firmware, device updates, installers, or field-deployed software.
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Experience with Windows application build and installer workflows.
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Experience with code signing, certificates, secure release processes, and software supply chain controls.
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Experience with cloud platforms such as Azure, AWS, or similar.
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Experience with infrastructure as code, containerization, virtual machines, or test environment automation.
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Experience with artifact repositories, package registries, release manifests, update servers, or deployment metadata.
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Experience defining or operating SLIs, SLOs, error budgets, incident response processes, or post-incident reviews.
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Experience creating internal developer platforms, golden paths, paved roads, or platform-as-a-product practices.
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Experience integrating AI-assisted development, test generation, workflow automation, or developer productivity tooling into the SDLC.
Focuses on reducing delivery risk, improving flow, increasing release confidence, and improving operational reliability rather than simply producing automation artifacts.
Platform Product Thinking
Treats internal tools, pipelines, workflows, templates, standards, and documentation as a product that engineers rely on every day.
Systems Thinking
Understands how source control, builds, tests, packages, releases, installers, hardware, environments, observability, and operations connect as one delivery system.
Balances speed with uptime, recoverability, auditability, traceability, and operational confidence.
Practical Automation
Turns repetitive, fragile, manual, or high-risk workflows into reliable automation while respecting real-world constraints.
Works effectively across software, hardware, QA, application engineering, field support, product, IT, and leadership without becoming a bottleneck.
Candidates do not need experience with every tool listed, but should be comfortable operating in similar environments.
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GitHub, GitHub Actions, pull requests, branch protection, repository standards
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CI/CD pipelines and reusable workflow automation
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PowerShell, Bash, Python, C#, JavaScript/TypeScript, or similar scripting/programming languages
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Build systems, artifact repositories, package registries, release metadata, and deployment manifests
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Windows installers, firmware packages, application packages, or device update workflows
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Test automation frameworks and test orchestration tools
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Azure, AWS, or similar cloud platforms
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Observability platforms, logging, metrics, dashboards, and alerting tools
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Secrets management, code signing, certificates, and secure automation patterns
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AI-assisted developer tooling, workflow generation, test generation, or code review tooling
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