Job Posting Title:
Director of AI Platforms, Texas Institute for Electronics
----
Hiring Department:
Texas Institute for Electronics
----
Position Open To:
All Applicants
----
Weekly Scheduled Hours:
40
----
FLSA Status:
To Be Determined at Offer
----
Earliest Start Date:
Ongoing
----
Position Duration:
Expected to Continue
----
Location:
AUSTIN, TX
----
Job Details:
General Notes
About TIE
Texas Institute for Electronics (TIE) is a transformative, well-funded semiconductor foundry venture combining the agility of a startup with the scale of a national initiative.
Our Mission A key part of our mission is to advance the state of the art in 3D heterogeneous integration (3DHI), chiplet-based architectures, and multi-component microsystems- catalyzing breakthroughs across microelectronics, artificial intelligence, quantum computing, high-performance computing, and next-generation healthcare devices.
Our Impact Backed by $1.4 billion in combined funding from DARPA, Texas state initiatives, and strategic partners, we are building foundational capabilities in advanced packaging and integrated design infrastructure to restore U.S. leadership in microelectronics manufacturing.
Our Technology Our 3DHI and chiplet integration platforms integrate novel thermal management and advanced interconnect solutions to deliver unprecedented performance and energy efficiency. Operating at the intersection of defense electronics and commercial markets, TIE offers a rare opportunity to reimagine an industry from the ground up and build transformative products with global impact.
UT Austin, recognized by Forbes as one of America’s Best Large Employers, provides outstanding employee benefits and total rewards packages that include:
- Competitive health benefits (employee premiums covered at 100%, family premiums at 50%)
- Voluntary Vision, Dental, Life, and Disability insurance options
- Generous paid vacation, sick time, and holidays
- Teachers Retirement System of Texas, a defined benefit retirement plan, with 8.25% employer matching funds
- Additional Voluntary Retirement Programs: Tax Sheltered Annuity 403(b) and a Deferred Compensation program 457(b)
- Flexible spending account options for medical and childcare expenses
- Robust free training access through LinkedIn Learning plus professional conference opportunities
- Tuition assistance
- Expansive employee discount program including athletic tickets
- Free access to UT Austin's libraries and museums with staff ID card
- Free rides on all UT Shuttle and Austin CapMetro buses with staff ID card
- For more details, please see Benefits | Human Resources and UT Austin Employee Experience | Human Resources and UT Austin Employee Experience | Human Resources
Purpose
Drive the design, deployment, and optimization of enterprise-grade LLM systems, ensuring scalable, secure, and high-performance AI solutions tailored to complex organizational needs. Lead technical innovation across architecture, MLOps, and retrieval-augmented generation to deliver impactful, privacy-compliant AI capabilities.
Responsibilities
- Define and lead the software architecture and implementation roadmap for a scalable, modular AI infrastructure platform. You will 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. You will be responsible for mentoring, hiring, and setting a culture of technical excellence and operational discipline.
- Own core services that power 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 across teams. You will define best practices and lead by example in system design, code reviews, and architectural discussions.
- Lead on-premise deployment strategy, ensuring our stack is optimized for hybrid environments. You will manage 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 align engineering priorities with product goals. You’ll help 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, putting in place monitoring, alerting, and incident response practices that ensure uptime and stability in enterprise-grade deployments.
- Stay on top of evolving technologies in distributed systems, containerization, service mesh, observability, and developer tooling—bringing in the best ideas to future-proof our platform.
Other related functions as assigned.
Required Qualifications
- BS in Computer Science, Engineering, or a related field.
- 8 or more years of software engineering experience, including 3 or more years focused on AI/ML and LLM-based applications.
- Deep knowledge of LLM architectures and tools – you understand transformer models inside and out and are fluent in the surrounding ecosystem (from tokenization and embedding techniques to prompt engineering and fine-tuning methods).
- Proven track record of productionizing LLM applications end-to-end. You have built and deployed AI-powered solutions (using both commercial APIs and open-source models) into real-world production environments – including experience with on-prem or private cloud deployments of AI systems.
- Hands-on experience with the LLM tech stack: this includes building pipelines with vector databases (for embedding storage/search) and using LLM orchestration frameworks like LangChain or LlamaIndex to compose prompts, tools, and data retrieval.
- Experience with modern model serving and scaling – familiarity with frameworks such as vLLM, LMDeploy, Ray (for distributed inference), or Triton Inference Server to optimize runtime performance of large models.
- Exceptional engineering and problem-solving skills. You can design elegant solutions for complex challenges and debug issues across the ML stack (data, model, infrastructure) when things go wrong.
- Excellent communication skills. You know how to articulate complex technical concepts clearly and adjust your message for engineers, founders, or other stakeholders. You can document architectures, write clear project plans, and mentor others by explaining the “why” behind technical decisions.
- You have the ability to work effectively in fast-paced environments. You have the ability to act with urgency, adapt quickly to new information, and take ownership of.
- Execution mindset. You have demonstrated experience driving projects forward in a hands-on role without heavy process or management overhead. You excel at managing multiple priorities, staying organized, and delivering results in a lean team setting.
Relevant education and experience may be substituted as appropriate.
Preferred Qualifications
- MS or PhD in Computer Science, Machine Learning, or a related discipline.
- Prior technical leadership experience. Experience leading an engineering team or serving as a tech lead for complex AI/ML projects. Ability to mentor others and experience managing project roadmaps or teams in previous roles.
- Domain expertise in NLP/LLMs. Publications, open-source contributions, or recognized expertise in the NLP/LLM field (e.g. contributions to Transformer libraries, research in language modeling, etc.) will set you apart.
- Enterprise AI experience. Familiarity with the unique challenges of applying AI in enterprise settings – such as handling sensitive data, ensuring compliance (e.g. GDPR, SOC2), or integrating with enterprise IT systems – is a plus.
Salary Range
TIE pays industry-competitive salaries
Working Conditions
- May work around standard office conditions
- Repetitive use of a keyboard at a workstation
- Use of manual dexterity (ex: using a mouse)
Work Shift
- Monday - Friday 8am to 5pm
- This position may involve work outside of regular business hours during peak periods.
- Occasional travel may be required.
Required Materials
- Resume/CV
- 3 work references with their contact information; at least one reference should be from a supervisor
- Letter of interest (optional)
Important for applicants who are NOT current university employees or contingent workers: You will be prompted to submit your resume the first time you apply, then you will be provided an option to upload a new Resume for subsequent applications. Any additional Required Materials (letter of interest, references, etc.) will be uploaded in the Application Questions section; you will be able to multi-select additional files. Before submitting your online job application, ensure that ALL Required Materials have been uploaded. Once your job application has been submitted, you cannot make changes.
Important for Current university employees and contingent workers: As a current university employee or contingent worker, you MUST apply within Workday by searching for Find UT Jobs. If you are a current University employee, log-in to Workday, navigate to your Worker Profile, click the Career link in the left hand navigation menu and then update the sections in your Professional Profile before you apply. This information will be pulled in to your application. The application is one page and you will be prompted to upload your resume. In addition, you must respond to the application questions presented to upload any additional Required Materials (letter of interest, references, etc.) that were noted above.
----
Employment Eligibility:
Regular staff who have been employed in their current position for the last six continuous months are eligible for openings being recruited for through University-Wide or Open Recruiting, to include both promotional opportunities and lateral transfers. Staff who are promotion/transfer eligible may apply for positions without supervisor approval.
----
Retirement Plan Eligibility:
The retirement plan for this position is Teacher Retirement System of Texas (TRS), subject to the position being at least 20 hours per week and at least 135 days in length. This position has the option to elect the Optional Retirement Program (ORP) instead of TRS, subject to the position being 40 hours per week and at least 135 days in length.
----
Background Checks:
A criminal history background check will be required for finalist(s) under consideration for this position.
----
Equal Opportunity Employer:
The University of Texas at Austin, as an equal opportunity/affirmative action employer, complies with all applicable federal and state laws regarding nondiscrimination and affirmative action. The University is committed to a policy of equal opportunity for all persons and does not discriminate on the basis of race, color, national origin, age, marital status, sex, sexual orientation, gender identity, gender expression, disability, religion, or veteran status in employment, educational programs and activities, and admissions.
----
Pay Transparency:
The University of Texas at Austin will not discharge or in any other manner discriminate against employees or applicants because they have inquired about, discussed, or disclosed their own pay or the pay of another employee or applicant. However, employees who have access to the compensation information of other employees or applicants as a part of their essential job functions cannot disclose the pay of other employees or applicants to individuals who do not otherwise have access to compensation information, unless the disclosure is (a) in response to a formal complaint or charge, (b) in furtherance of an investigation, proceeding, hearing, or action, including an investigation conducted by the employer, or (c) consistent with the contractor’s legal duty to furnish information.
----
Employment Eligibility Verification:
If hired, you will be required to complete the federal Employment Eligibility Verification I-9 form. You will be required to present acceptable and original documents to prove your identity and authorization to work in the United States. Documents need to be presented no later than the third day of employment. Failure to do so will result in loss of employment at the university.
----
E-Verify:
The University of Texas at Austin use E-Verify to check the work authorization of all new hires effective May 2015. The university’s company ID number for purposes of E-Verify is 854197. For more information about E-Verify, please see the following:
----
Compliance:
Employees may be required to report violations of law under Title IX and the Jeanne Clery Disclosure of Campus Security Policy and Crime Statistics Act (Clery Act). If this position is identified a Campus Security Authority (Clery Act), you will be notified and provided resources for reporting. Responsible employees under Title IX are defined and outlined in HOP-3031.
The Clery Act requires all prospective employees be notified of the availability of the Annual Security and Fire Safety report. You may access the most recent report here or obtain a copy at University Compliance Services, 1616 Guadalupe Street, UTA 2.206, Austin, Texas 78701.
DevOps & Infrastructure (2)