The University of Texas at Austin
Director of Software Engineering, Texas Institute of Electronics
The University of Texas at Austin, Austin, Texas, us, 78716
Overview
Director of Software Engineering, Texas Institute of Electronics, at The University of Texas at Austin. The Director will lead the architecture and implementation of a scalable AI infrastructure platform, managing a high-caliber engineering team to ensure performance, security, and reliability while collaborating cross-functionally to align engineering priorities with product goals. Responsibilities
Define and lead the software architecture and implementation roadmap for a scalable, modular AI infrastructure platform. Work across backend, orchestration, and deployment layers—focusing on performance, security, and reliability. Build and manage a high-caliber engineering team, including backend developers, platform engineers, and site reliability engineers. Mentor, hire, and foster a culture of technical excellence and operational discipline. Own core services powering AI pipelines, including APIs for data ingestion and transformation, orchestration of model inference jobs, and integration with LLM orchestration layers and vector stores. Establish technical strategy and design standards that support rapid prototyping, automated testing, and code reuse; lead by example in system design, code reviews, and architectural discussions. Lead on-premise deployment strategy, optimizing for hybrid environments and managing challenges around air-gapped deployments, resource management, and update rollouts in constrained environments. Collaborate cross-functionally with AI engineering, product management, and customer success to translate high-level needs into deliverable milestones. Implement and maintain CI/CD pipelines and DevOps best practices, focusing on security, observability, rollback safety, and developer productivity. Develop and enforce SLAs/SLOs for critical services with monitoring, alerting, and incident response practices to ensure uptime and stability in enterprise deployments. Stay current with evolving technologies in distributed systems, containerization, service mesh, observability, and developer tooling to future-proof the platform. Other related functions as assigned. Required Qualifications
Bachelor of Science in Computer Science or related field; strong systems programming fundamentals. 8+ years of software engineering experience, with at least 3 years in technical leadership or management. Track record building and deploying distributed backend systems at scale with emphasis on reliability and maintainability. Deep experience with containerized environments and orchestration (Kubernetes, Helm, Docker). Fluency in modern backend languages (Go, Rust, Python, or Java). Hands-on experience managing hybrid or on-prem enterprise software deployments; troubleshooting networking, storage, and related issues. Demonstrated ability to lead high-performance engineering teams with hiring, mentoring, and engineering excellence focus. Strong communication and documentation skills for diverse audiences; ability to align stakeholders toward common goals. Startup mindset; comfortable working hands-on across multiple layers of the tech stack. Relevant education/experience may be substituted as appropriate. Preferred Qualifications
Master of Science in Computer Science or Engineering. Experience building data-intensive or ML-heavy software products, including APIs for inference, data pipelines, and real-time analytics. Familiarity with API design (REST/gRPC/OpenAPI) and interfacing with AI model serving stacks (e.g., vLLM, Triton, TorchServe). Experience with secure software development and compliance in regulated environments (SOC2, HIPAA, FedRAMP). Contributions to open-source projects or strong technical thought leadership in cloud-native or ML infrastructure communities. Experience building platforms or developer tools used by external customers or partners. Salary
TIE pays industry-competitive salaries. Working Conditions
May work around standard office conditions Repetitive keyboard use at a workstation Use of manual dexterity (e.g., using a mouse) Required Materials
Resume/CV 3 work references with contact information; at least one reference should be from a supervisor Letter of interest (optional) Important for Applicants
Applicants not current UT employees or contingent workers will be prompted to submit their resume; additional required materials (letter of interest, references) will be uploaded in Application Questions. Before submitting, ensure all required materials are uploaded. Once submitted, changes are not allowed. Important for Current UT Employees and Contingent Workers
Current UT employees must apply within Workday by locating Find UT Jobs and updating their Professional Profile as applicable. The application is one page and will prompt you to upload your resume and any additional required materials. Employment Eligibility & Compliance
Background checks, E-Verify, and equal opportunity statements apply as described by UT Austin policies; as an equal opportunity employer, UT Austin complies with applicable laws regarding nondiscrimination and affirmative action.
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Director of Software Engineering, Texas Institute of Electronics, at The University of Texas at Austin. The Director will lead the architecture and implementation of a scalable AI infrastructure platform, managing a high-caliber engineering team to ensure performance, security, and reliability while collaborating cross-functionally to align engineering priorities with product goals. Responsibilities
Define and lead the software architecture and implementation roadmap for a scalable, modular AI infrastructure platform. Work across backend, orchestration, and deployment layers—focusing on performance, security, and reliability. Build and manage a high-caliber engineering team, including backend developers, platform engineers, and site reliability engineers. Mentor, hire, and foster a culture of technical excellence and operational discipline. Own core services powering AI pipelines, including APIs for data ingestion and transformation, orchestration of model inference jobs, and integration with LLM orchestration layers and vector stores. Establish technical strategy and design standards that support rapid prototyping, automated testing, and code reuse; lead by example in system design, code reviews, and architectural discussions. Lead on-premise deployment strategy, optimizing for hybrid environments and managing challenges around air-gapped deployments, resource management, and update rollouts in constrained environments. Collaborate cross-functionally with AI engineering, product management, and customer success to translate high-level needs into deliverable milestones. Implement and maintain CI/CD pipelines and DevOps best practices, focusing on security, observability, rollback safety, and developer productivity. Develop and enforce SLAs/SLOs for critical services with monitoring, alerting, and incident response practices to ensure uptime and stability in enterprise deployments. Stay current with evolving technologies in distributed systems, containerization, service mesh, observability, and developer tooling to future-proof the platform. Other related functions as assigned. Required Qualifications
Bachelor of Science in Computer Science or related field; strong systems programming fundamentals. 8+ years of software engineering experience, with at least 3 years in technical leadership or management. Track record building and deploying distributed backend systems at scale with emphasis on reliability and maintainability. Deep experience with containerized environments and orchestration (Kubernetes, Helm, Docker). Fluency in modern backend languages (Go, Rust, Python, or Java). Hands-on experience managing hybrid or on-prem enterprise software deployments; troubleshooting networking, storage, and related issues. Demonstrated ability to lead high-performance engineering teams with hiring, mentoring, and engineering excellence focus. Strong communication and documentation skills for diverse audiences; ability to align stakeholders toward common goals. Startup mindset; comfortable working hands-on across multiple layers of the tech stack. Relevant education/experience may be substituted as appropriate. Preferred Qualifications
Master of Science in Computer Science or Engineering. Experience building data-intensive or ML-heavy software products, including APIs for inference, data pipelines, and real-time analytics. Familiarity with API design (REST/gRPC/OpenAPI) and interfacing with AI model serving stacks (e.g., vLLM, Triton, TorchServe). Experience with secure software development and compliance in regulated environments (SOC2, HIPAA, FedRAMP). Contributions to open-source projects or strong technical thought leadership in cloud-native or ML infrastructure communities. Experience building platforms or developer tools used by external customers or partners. Salary
TIE pays industry-competitive salaries. Working Conditions
May work around standard office conditions Repetitive keyboard use at a workstation Use of manual dexterity (e.g., using a mouse) Required Materials
Resume/CV 3 work references with contact information; at least one reference should be from a supervisor Letter of interest (optional) Important for Applicants
Applicants not current UT employees or contingent workers will be prompted to submit their resume; additional required materials (letter of interest, references) will be uploaded in Application Questions. Before submitting, ensure all required materials are uploaded. Once submitted, changes are not allowed. Important for Current UT Employees and Contingent Workers
Current UT employees must apply within Workday by locating Find UT Jobs and updating their Professional Profile as applicable. The application is one page and will prompt you to upload your resume and any additional required materials. Employment Eligibility & Compliance
Background checks, E-Verify, and equal opportunity statements apply as described by UT Austin policies; as an equal opportunity employer, UT Austin complies with applicable laws regarding nondiscrimination and affirmative action.
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