Security Engineer

Job Description Are you interested in working for a company that fosters growth opportunities, community involvement and a team oriented atmosphere? Clark Dietrich fosters a work-life balance and offers competitive compensation and benefits. Join a team that is STRONGER THAN STEEL, by applying to become a Security Engineer at our West Chester, OH location.

POSITION SUMMARY: The Security Engineer designs, implements, and operates security controls across identity, endpoints, networks, cloud services, applications, data, and AI-enabled systems. This hands-on role reduces cyber risk while enabling the responsible use of modern technology. The engineer partners with infrastructure, application, data, legal, privacy, and business teams to build resilient services, detect and respond to threats, and translate technical findings into practical action.

KEY RESPONSIBILITIES Security architecture and engineering

Engineer secure platforms. Design and improve preventive, detective, and recovery controls across hybrid infrastructure, SaaS, cloud platforms, endpoints, networks, and business applications.

Strengthen identity. Implement MFA, Conditional Access, least privilege, privileged-access management, lifecycle governance, service-identity controls, and periodic access reviews.

Secure cloud-native workloads. Assess cloud configurations, APIs, containers, serverless services, infrastructure as code, CI/CD pipelines, secrets, and software supply-chain dependencies.

Manage exposure. Identify and prioritize vulnerabilities, attack paths, misconfigurations, unsupported assets, and internet-facing risk; partner with owners through verified remediation.

Embed security by design. Perform architecture reviews and threat modeling for new systems, integrations, vendors, and major changes; define proportionate control requirements before production.

AI and emerging-technology security

Govern enterprise AI use. Maintain visibility into approved and unapproved AI services, models, agents, copilots, plugins, data connections, and business use cases; help teams choose secure, sanctioned paths.

Secure AI systems end to end. Review data flows, model and API access, retrieval pipelines, vector stores, prompts, tools, memory, outputs, and human-approval points across the AI lifecycle.

Threat-model AI-specific abuse. Evaluate prompt injection, sensitive-data disclosure, insecure output handling, model or data poisoning, excessive agency, identity abuse, supply-chain compromise, denial of service, and unsafe tool execution.

Implement AI guardrails. Apply strong authentication, scoped authorization, data classification and DLP, secrets management, content filtering, tool isolation, rate limits, audit logging, monitoring, and human-in-the-loop controls.

Validate before and after launch. Coordinate security testing, adversarial evaluation, red teaming, misuse-case testing, and ongoing monitoring for AI applications and material model, prompt, tool, or data changes.

Enable safe adoption. Translate AI policy into usable engineering standards and employee guidance; investigate shadow-AI and risky data-handling patterns without defaulting to indiscriminate blocking.

Use AI responsibly in security operations. Apply automation and AI-assisted analysis to accelerate triage, investigation, detection development, and reporting while preserving evidence, access controls, validation, and accountable human decisions.

Detection, incident response, and resilience

Improve detection. Engineer useful telemetry and detections across identity, endpoint, network, cloud, email, applications, data, and AI services; tune alerting to improve signal quality.

Respond to incidents. Lead or support triage, containment, eradication, recovery, evidence preservation, communications, and post-incident improvement, including identity and token compromise.

Automate repeatable work. Build scripts, queries, playbooks, and integrations that improve investigation speed, control consistency, and operational visibility.

Protect recovery paths. Validate hardening, patching, backup security, recovery procedures, and disaster-recovery assumptions through exercises and technical testing.

Share actionable intelligence. Produce decision-ready metrics and concise reporting on exposure, incidents, control health, AI risk, remediation performance, and residual risk.

Governance and collaboration

Maintain security standards, diagrams, procedures, playbooks, risk decisions, and operational records that are clear enough for…