The AI Agent Engineering & DevSecOps Automation Engineer is a senior engineering professional responsible for designing, developing, and operationalizing AI agents that automate DevSecOps control, engineering, and execution functions across the enterprise. This role builds secure, scalable, and reusable agentic capabilities that improve software delivery, strengthen control execution, and reduce manual effort across engineering teams.

The engineer develops agentic workflows and platform integrations that support control validation, policy enforcement, evidence collection, pipeline execution, application security remediation, infrastructure automation, engineering support, and platform operations. The role integrates AI capabilities with enterprise DevSecOps platforms, APIs, event-driven services, automation frameworks, and observability tooling while applying secure development, human oversight, auditability, and policy-as-code practices.

Success in this role requires strong software engineering fundamentals, hands-on Python and API development, experience with modern AI and LLM frameworks, and practical knowledge of DevSecOps, platform engineering, automation, and regulated technology environments. The engineer translates complex engineering and control requirements into production-grade agents and services that are measurable, maintainable, observable, and safe to operate at enterprise scale.

ESSENTIAL DUTIES AND RESPONSIBILITIES
Following is a summary of the essential functions for this job. Other duties may be performed, both major and minor, which are not mentioned below. Specific activities may change from time to time.
1. Designs, builds, and implements enterprise infrastructure technology platforms and systems across cloud, network, database, storage, platform, computing, or middleware domains.
2. Applies automation, monitoring, and optimization techniques to ensure high availability and performance of infrastructure.
3. Manages infrastructure engineering projects and processes aligned with organizational policies and regulatory requirements.
4. Collaborates with cross-functional teams and stakeholders to integrate new technologies and continuously improve infrastructure standards and processes.
5. Troubleshoots and resolves complex technical issues impacting infrastructure performance and reliability.
6. Supports compliance with technology strategies, standards, and governance to mitigate risks and ensure regulatory adherence.
7. Reviews and guides infrastructure designs, configurations, and procedures to support operational consistency and knowledge sharing.
8. Provides technical guidance and direction to lower-level technical professionals.
9. Develops technical solutions with deep analysis of infrastructure systems and leads infrastructure processes.

Qualifications
Required Qualifications
The requirements listed below are representative of the knowledge, skill and/or ability required. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.
1. Bachelor's degree in Computer Science, Engineering, Information Systems, a related field, or equivalent education, training, and work-related experience.
2. Minimum of 5 years of professional experience in infrastructure engineering.
3. Strong knowledge of enterprise infrastructure technologies including cloud, network, database, storage, platform, computing, and middleware.

Preferred Qualifications
1. Bachelor's degree and six or more years of experience in software engineering, platform engineering, DevSecOps, intelligent automation, or an equivalent combination of education and experience.

2. Hands-on experience designing and developing AI agents, agentic workflows, copilots, or autonomous automation solutions using modern LLM and orchestration frameworks.

3. Strong proficiency in Python and API-first software development, including production-grade services, automated testing, secure coding, and integration patterns.

4. Experience with retrieval-augmented generation, tool use, structured outputs, prompt and context management, model evaluation, and safeguards for enterprise AI solutions.

5. Experience integrating AI agents with GitLab CI/CD, Ansible Automation Platform, infrastructure-as-code, policy-as-code, application security tooling, artifact repositories, and engineering workflow platforms.

6. Experience automating DevSecOps controls such as policy enforcement, security scanning, control validation, evidence collection, exception handling, and remediation workflows.

7. Working knowledge of REST APIs, event-driven architectures, Git-based development, containers, Kubernetes or OpenShift, and cloud or on-premises platform environments.

8. Experience implementing agent observability, audit logging, telemetry, evaluation metrics, human-in-the-loop controls, access controls, and operational monitoring using OpenTelemetry, Splunk, Grafana, or similar technologies.

9. Ability to translate complex engineering, risk, and control requirements into reusable automation that is secure, explainable, maintainable, and suitable for a regulated enterprise environment.

10. Experience working in a regulated financial services, healthcare, or government environment where compliance constraints directly shape technical architecture decisions.

11. Strong problem-solving and communication skills with the ability to explain technical complexity in plain language and collaborate across engineering, cybersecurity, risk, architecture, and product teams.