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About This Role
Advanced Systems Design is seeking an AI Platform Engineer for our client located in Montgomery, AL.
This position is onsite in Montgomery, AL, and requires in-person availability starting day 1.
Job Overview:
The ideal candidate for this role is an early-career to mid-level engineer with strong cloud, SaaS, systems, application, automation, or platform administration fundamentals who has practical exposure to modern AI services and wants to work hands-on across multiple enterprise AI platforms.
The AI Platform Engineer is a hands-on technical position responsible for configuring, administering, integrating, operating, and supporting enterprise Artificial Intelligence (AI) platforms and related services. The position will initially work across OpenAI/ChatGPT, Microsoft Copilot, and Google Gemini capabilities, with responsibilities expanding as additional AI platforms are adopted. The engineer helps turn AI products into reliable enterprise services by supporting access, integrations, automation, monitoring, documentation, security controls, governance requirements, and day-to-day technical operations. This is not primarily a consulting, data-science, or custom model-training role; it is an enterprise platform engineering and service-support role.
Responsibilities:
- Administer and support enterprise AI platforms, initially including OpenAI/ChatGPT, Microsoft Copilot, and Google Gemini.
- Configure platform settings, environments, tenants/workspaces, user and group access, permissions, roles, service controls, and approved feature availability.
- Assist with implementation of APIs, connectors, agents, automation, plugins/extensions, knowledge sources, and integrations between AI platforms and enterprise systems.
- Support identity and access requirements including role-based access, service identities, OAuth/OIDC or SAML-based integrations, and least-privilege access patterns as applicable.
- Troubleshoot platform, configuration, authentication, authorization, integration, data-access, performance, and service-availability issues.
- Monitor service health, utilization, licensing, consumption, logs, alerts, platform changes, and operational metrics; escalate and coordinate issues when needed.
- Assist with provisioning, modification, change, release, and retirement processes for AI services, agents, integrations, and related technical components.
- Maintain inventories of approved AI services, applications, agents, integrations, service accounts, knowledge sources, and technical dependencies.
- Implement approved security, privacy, data-protection, governance, records, and compliance requirements within supported AI platforms.
- Participate in technical reviews and testing of new AI use cases, platform features, models, agents, and service changes before broader deployment.
- Create and maintain technical documentation, configuration records, support procedures, runbooks, knowledge articles, and operational standards.
- Provide Tier 2/Tier 3 support for AI-related incidents and service requests and coordinate with vendors or internal teams for escalation.
- Develop scripts, workflows, or lightweight automation using PowerShell, Python, APIs, or comparable tools to improve repeatability and supportability.
- Work collaboratively with cybersecurity, cloud/infrastructure, identity, application, data, service-management, legal/compliance, and business teams.
Required Qualifications:
- Approximately 2 - 4 years of professional experience in cloud, systems, platform, application, DevOps, automation, enterprise SaaS administration, technical support, or a closely related engineering discipline.
- At least one year of meaningful exposure to AI, intelligent automation, generative AI, or AI-enabled enterprise platforms; equivalent hands-on project experience may be considered.
- Hands-on experience with at least one major AI ecosystem and the ability to learn and support additional platforms.
- Working knowledge of APIs, identity/access concepts, cloud/SaaS administration, troubleshooting, change management, and technical documentation.
- Ability to work in a service-oriented environment and support both technical teams and end users while maintaining appropriate security and governance controls.
- Ability to learn rapidly changing AI platforms and evaluate the operational impact of new features and models.
- Strong troubleshooting and root-cause analysis skills across cloud services, identity, APIs, integrations, and enterprise applications.
- Ability to translate policy or control requirements into practical configuration, support, and operating procedures.
- Clear technical writing and documentation skills with an emphasis on repeatability and supportability.
- Ability to manage multiple tasks, prioritize incidents and service requests, and communicate effectively with technical and nontechnical stakeholders.
Preferred Qualifications:
- Experience with OpenAI/ChatGPT Enterprise or Business, OpenAI APIs, Microsoft Copilot/Copilot Studio/Azure AI services, or Google Gemini/Vertex AI/Google Workspace.
- Experience supporting APIs, connectors, AI agents, retrieval-augmented generation (RAG), enterprise search, knowledge grounding, or related integration patterns.
- Experience with PowerShell, Python, REST APIs, JSON, scripting, workflow automation, or low-code automation platforms.
- Experience with Microsoft Entra ID, Google identity, IAM/RBAC concepts, service principals/service accounts, secrets, permissions, or access reviews.
- Familiarity with enterprise security, privacy, data-loss prevention, records/data handling, responsible AI, and AI governance requirements.
- Experience using IT service management practices for incident, request, change, problem, knowledge, and service operations.
Education Required:
- Bachelor's degree in Computer Science, Information Systems, Engineering, Cybersecurity, or a related technical discipline, or an equivalent combination of education and relevant professional experience.
Advanced Systems Design, Inc. is:
- A leading Information Technology provider for Federal, State & Local government agencies.
- A certified minority-owned small business government contractor with capabilities related to Public Health IT, Criminal Justice, Transportation, and Defense.
- A certified service-disabled veteran-owned company with a proud 46-year track record of providing successful innovative solutions for our government customers.
- A drug-free workplace in accordance with the Drug-Free Workplace Act of 1988.
- Applicants who have a signed offer of employment or contractor agreement are subject to: + the pre-employment testing protocol:
- background investigation
- drug screening
Our Employees:
- Are actively working on next-generation technology projects with the U.S. Department of Veterans Affairs, CDC, and a wide array of Federal, State, and Local agencies throughout the United States
- Are eligible for wide-ranging benefits and perks, including but not limited to: + Comprehensive Health Insurance with PPO and HDHP/HSA options + Dental Insurance + Vision Insurance + Short/Long-Term Disability + Group Life Insurance - Company Paid + Voluntary Life Insurance + 401(k) Plan with Employer Match + Paid Time Off (Vacation/Sick) + Holiday Pay - Company Paid Federal Holidays + Professional Certification Incentive Plan + Employee Referral Plan + Technology Exposure
For additional information regarding Advanced Systems Design, please check out our WEBSITE or click HERE for all current job openings.
Advanced Systems Design is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran.
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 Advanced Systems Design, 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.
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.
Advanced Systems Design AI Hiring
Advanced Systems Design has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Montgomery, AL, US.
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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