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About This Role
About Us
Georgia Tech prides itself on its technological resources, collaborations, high-quality student body, and its commitment to building an outstanding and diverse community of learning, discovery, and creation. We strongly encourage applicants whose values align with our institutional values, as outlined in our strategic plan. These values include academic excellence, diversity of thought and experience, inquiry and innovation, collaboration and community, and ethical behavior and stewardship. Georgia Tech has policies to promote a healthy work-life balance and is aware that attracting faculty may require meeting the needs of two careers.
About Georgia Tech
Georgia Tech is a top-ranked public research university situated in the heart of Atlanta, a diverse and vibrant city with numerous economic and cultural strengths. The Institute serves more than 45,000 students through top-ranked undergraduate, graduate, and executive programs in engineering, computing, science, business, design, and liberal arts. Georgia Tech's faculty attracted more than $1.4 billion in research awards this past year in fields ranging from biomedical technology to artificial intelligence, energy, sustainability, semiconductors, neuroscience, and national security. Georgia Tech ranks among the nation's top 20 universities for research and development spending and No. 1 among institutions without a medical school.
Georgia Tech's Mission and Values
Georgia Tech's mission is to develop leaders who advance technology and improve the human condition. The Institute has nine key values that are foundational to everything we do:
- Students are our top priority.
- We strive for excellence.
- We thrive on diversity.
- We celebrate collaboration.
- We champion innovation.
- We safeguard freedom of inquiry and expression.
- We nurture the wellbeing of our community.
- We act ethically.
- We are responsible stewards.
Over the next decade, Georgia Tech will become an example of inclusive innovation, a leading technological research university of unmatched scale, relentlessly committed to serving the public good; breaking new ground in addressing the biggest local, national, and global challenges and opportunities of our time; making technology broadly accessible; and developing exceptional, principled leaders from all backgrounds ready to produce novel ideas and create solutions with real human impact.
The Office of Information Technology (OIT) provides information technology leadership and support to the Georgia Institute of Technology, working in partnership with academic and business units to meet the unique needs of a leading research university. OIT serves as the primary source of enterprise-wide information technology and telecommunications services in support of students, faculty, staff, and researchers.
Location
Atlanta, GA- In office. On-site
Job Summary
The AI and Automation Architect is responsible for designing, governing, and advancing the architectural frameworks, platforms, integration patterns, and technical controls that enable secure, scalable, and responsible artificial intelligence and intelligent automation across the Institute. This role establishes enterprise architecture standards, governance practices, and lifecycle controls that support academic, research, student, and administrative initiatives while ensuring alignment with institutional requirements for data governance, privacy, cybersecurity, accessibility, compliance, reliability, and risk management.
Job Summary
This position is at the level of Lead Professional, emphasizing enterprise-scale architecture and deep technical expertise that informs and enables institutional AI, data, governance, and platform strategies set by senior leadership. For more information on this level, please visit URL https://publish.smartsheet.com/4a3a97a4b2154516ae722437d46fa038
Responsibilities
Job Duty 1 -
Design cloud, integration, and platform architecture standards that connect AI, machine learning, and automation solutions with enterprise data, systems, and applications.
Job Duty 2 -
Define and maintain security architecture and controls: identity, access, network isolation, encryption, secrets management, and data-loss prevention for the enterprise AI and automation platform.
Job Duty 3 -
Implement technical safeguards against AI-specific risks, including prompt injection, sensitive-data exposure, insecure tool use, excessive agent permissions, model or supply-chain compromise, and unauthorized AI services.
Job Duty 4 -
Evaluate AI, cloud, integration, and automation technologies using documented criteria for capability, security, privacy, accessibility, risk, supportability, continuity, and cost.
Job Duty 5 -
Architect data retrieval and grounding strategies, including retrieval-augmented generation and data pipelines, that allow AI and automation tools to consume Institute data accurately and securely, in alignment with data governance standards.
Job Duty 6 -
Lead collaborative design sessions and architecture reviews to align AI and automation solutions with institutional goals, stakeholder needs, and approved architecture standards.
Job Duty 7 -
Translate AI governance requirements into enforceable architecture standards, documentation practices, technical controls, and lifecycle processes for AI models, automation workflows, and integration components.
Job Duty 8 -
Provide technical guidance, mentoring, and training on cloud, AI, and automation architecture practices, and maintain current knowledge of emerging technologies.
Job Duty 9 -
Oversee architecture assessments and define platform requirements for capacity, performance, resilience, disaster recovery, scalability, service objectives, and observability for the enterprise AI and automation platform.
Job Duty 10 -
Perform other duties as assigned.
Required Qualifications
Educational Requirements
Bachelor's degree in related discipline or equivalent, related experience
Required Experience
6+ years of relevant experience
Preferred Qualifications
Preferred Educational Qualifications
Master's degree in related discipline or equivalent, related experience
Preferred Qualifications
Preferred Experience
Experience designing or operating production AI, machine learning, generative AI, or intelligent automation capabilities in a complex organization.
Experience architecting agentic AI systems, including tool integration, orchestration, and human oversight controls.
Experience with major cloud platforms (e.g., AWS, Azure, Google Cloud) and enterprise API or integration frameworks.
Experience establishing architecture standards, reference architectures, or review processes adopted across multiple teams.
Experience translating governance, security, privacy, or compliance requirements into technical controls and auditable evidence.
Experience supporting higher education, research, healthcare, government, or large enterprise environments.
Proposed Salary
$129,000 to $180,000 depending on experience and preferred skills.
Knowledge, Skills, & Abilities
Strong knowledge of enterprise and solution architecture principles, including distributed systems, integration patterns, and reference architecture design.
Strong understanding of how AI models and agents behave in production, including non-deterministic outputs, failure modes, evaluation methods, and performance variability.
Strong understanding of identity, authorization, and least-privilege principles as they apply to non-human actors such as service accounts, automations, and AI agents.
Working knowledge of data architecture concepts, including modeling, metadata, lineage, and categorization, sufficient to partner with data engineering, data platforms, and governance teams.
Working knowledge of privacy, accessibility, and records management obligations as they translate into system design constraints.
Knowledge of cloud consumption and cost models sufficient to design solutions that are financially transparent and sustainable at scale.
Ability to design architectures that accommodate rapid change in models, vendors, and tooling while minimizing lock-in and rework.
Ability to weigh competing tradeoffs across security, usability, cost, and speed of delivery, and to document decisions and rationale clearly.
Ability to earn credibility with engineering, security, privacy, data, and governance partners and to drive adoption of standards without direct authority.
Excellent written and verbal communication skills, with the ability to explain architectural and risk tradeoffs to executive and non-technical audiences.
Strong analytical, problem-solving, and organizational skills, with discretion in handling sensitive information.
USG Core Values
The University System of Georgia is comprised of our 25 institutions of higher education and learning as well as the System Office. Our USG Statement of Core Values are Integrity, Excellence, Accountability, and Respect. These values serve as the foundation for all that we do as an organization, and each USG community member is responsible for demonstrating and upholding these standards. More details on the USG Statement of Core Values and Code of Conduct are available in USG Board Policy 8.2.18.1.2 and can be found on-line at https://www.usg.edu/policymanual/section8/C224/#p8.2.18_personnel_conduct.
Additionally, USG supports Freedom of Expression as stated in Board Policy 6.5 Freedom of Expression and Academic Freedom found on-line at https://www.usg.edu/policymanual/section6/C2653.
Equal Employment Opportunity
The Georgia Institute of Technology (Georgia Tech) is an Equal Employment Opportunity Employer. The Institute is committed to maintaining a fair and respectful environment for all. To that end, and in accordance with federal and state law, Board of Regents policy, and Institute policy, Georgia Tech provides equal opportunity to all faculty, staff, students, and all other members of the Georgia Tech community, including applicants for admission and/or employment, contractors, volunteers, and participants in institutional programs, activities, or services. Georgia Tech complies with all applicable laws and regulations governing equal opportunity in the workplace and in educational activities.
Equal opportunity and decisions based on merit are fundamental values of the University System of Georgia ("USG") and Georgia Tech. Georgia Tech prohibits discrimination, including discriminatory harassment, on the basis of an individual's race, ethnicity, ancestry, color, religion, sex (including pregnancy), national origin, age, disability, genetics, or veteran status in its programs, activities, employment, and admissions. Further, Georgia Tech prohibits citizenship status, immigration status, and national origin discrimination in hiring, firing, and recruitment, except where such restrictions are required in order to comply with law, regulation, executive order, or Attorney General directive, or where they are required by Federal, State, or local government contract.
Other Information
This is not a supervisory position.
This is a position of trust (access to sensitive data).
This position will not be required to drive an Institute vehicle.
This position will not be required to travel.
This position does not have any direct financial responsibilities.
This position does not require a purchasing card (P-Card).
This position does not require security clearance.
Background Check
Successful candidate must be able to pass a background check. Please visit https://usg.policystat.com/policy/19298143/latest/
Salary Context
This $129K-$180K range is below the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Georgia Tech, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($154K) sits 27% below the category median. Disclosed range: $129K to $180K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Georgia Tech AI Hiring
Georgia Tech has 6 open AI roles right now. They're hiring across Research Engineer, AI/ML Engineer, Research Scientist. Positions span Atlanta, GA, US, Smyrna, GA, US. Compensation range: $110K - $180K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (4,384) and senior (3,343) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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