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About This Role
##### Who We Are:
Our team, the Cloud Services team within the Information Technology Center, is responsible for EntraID (formerly Azure)/M365 cloud services and Active Directory for the Enterprise. The team provides support and implementation services to the entire organization's IT infrastructure.
##### Objectives of this Role:
- Manage and enhance internal Public Key Infrastructure (PKI), ADFS (Active Directory Federation Services), and Certificate Lifecycle Management (e.g. Keyfactor Command) to maintain secure, reliable access to enterprise resources.
- Design and implement secure identity and access patterns for Azure and AWS services, including Azure OpenAI, Azure AI Hub/Foundry, Azure Cognitive Services, and AWS Bedrock.
- Lead the design, configuration, and maintenance of Entra ID (Azure AD) app registrations, service principals, and RBAC models that provide controlled, auditable access to cloud and AI workloads.
- Collaborate across IT teams (Cloud, Security, Networking, Applications, Help Desk) to resolve complex issues related to AD, Entra ID, ADFS, PKI, certificates, and cloud identity.
- Drive best practices for identity and access management, Azure and AWS infrastructure governance, and operational scalability across cloud and on\-premises environments.
- Maintain strong change control discipline and keep supporting documentation up to date.
##### Daily and Monthly Responsibilities:
- Manage and support internal PKI and certificate lifecycle processes, including: Administration of CAs, OCSP/CRL endpoints, and certificate trust chains.
- Configuration and operation of Keyfactor Command certificate discovery, issuance, renewal, and revocation.
- Support and maintain identity and federation services, including Active Directory (user and group management, roles, delegation), Entra ID app registrations, service principals, Conditional Access, RBAC, ADFS configuration and integration with internal and external applications.
- Collaborate with IT teams to resolve helpdesk tickets related to: AD and Entra ID authentication and authorization issues.
- ADFS sign\-on/federation problems and claims troubleshooting.
- Certificate deployment, trust, and lifecycle problems impacting applications and services.
- Implement and maintain secure access for Azure and AWS AI platforms, including: App registrations and security group\-based access controls for Azure OpenAI, Azure AI Hub/Foundry, and Azure Cognitive Services.
- IAM roles, policies, and network/security configuration for AWS service.
- Maintain documentation of identity architectures, PKI designs, AI enablement patterns, and change records for audits and future engineers, and research and propose improvements to identity, PKI, certificate lifecycle, and AI enablement, including automation and governance tooling.
##### Requirements:
- Requires a Bachelors degree in Information Technology or related degree field with relevant experience. In lieu of a Bachelors degree, 10 years of professional level experience, a high school education or equivalent with related certifications will be considered.
- Related Microsoft certifications are preferred.
- 6 years: Hands\-on experience managing enterprise identity platforms, including: Active Directory domain services. Entra ID (Azure AD) app registrations, service principals, and Conditional Access. ADFS design, configuration, and support.
- 3 years: Managing a PKI environment and certificate lifecycle, including: Internal CA infrastructure, OCSP/CRL, and certificate templates.
- Demonstrated experience designing and securing access for Azure and/or AWS solutions, including at least some of the following: Azure OpenAI, Azure AI Hub/Foundry, Azure Cognitive Services. AWS Bedrock or similar AWS AI/ML services.
- Strong understanding of identity and access management best practices, including: Role\-based access control (RBAC) in Azure and AWS. Least\-privilege design, MFA, and Conditional Access. Governance and operational scalability in cloud environments.
- A valid/clear driver's license is required.
##### Special Requirements:
Applicant selected will be subject to a government security investigation and must meet eligibility requirements for access to classified information. Applicant must be a U.S. citizen.
An Equal Employment Opportunity Employer: race, color, religion, sex, national origin, disability, and veteran status.
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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Southwest Research Institute, 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Southwest Research Institute AI Hiring
Southwest Research Institute has 2 open AI roles right now. They're hiring across Research Engineer, AI/ML Engineer. Based in San Antonio, TX, US.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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