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About This Role
Advanced Systems Design is seeking an Artificial Intelligence ("AI") Architect 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 Senior AI Platform & Services Engineer is a senior hands-on technical position responsible for engineering, administering, integrating, operating, and continuously improving enterprise AI platforms and services. The position provides technical leadership across a multi-platform AI environment initially centered on OpenAI/ChatGPT, Microsoft Copilot, and Google Gemini. The engineer establishes repeatable technical patterns, operational standards, integrations, access controls, monitoring, service-management processes, and governance implementation so AI capabilities can be operated securely and reliably as managed enterprise services. This role is expected to remain hands-on and is not primarily a consulting, policy-only, or custom model-research position.
Ideal Candidate Profile: An experienced enterprise platform engineer who can work deeply in the backend while also designing the service structure around AI. The ideal candidate can engineer integrations and controls, own complex troubleshooting, operationalize governance requirements, establish standards, and mentor less-experienced AI platform resources.
Primary Responsibilities:
- Engineer, administer, and continuously improve enterprise AI platforms and the supporting technical services required to operate them at scale.
- Serve as a hands-on technical subject-matter resource across OpenAI/ChatGPT, Microsoft Copilot, Google Gemini, and related enterprise AI technologies.
- Design and implement technical patterns for APIs, connectors, agents, orchestration, knowledge sources, retrieval- augmented generation (RAG), automation, and integrations with enterprise systems.
- Establish platform administration, environment management, access control, configuration, service onboarding, change/release, and lifecycle-management standards.
- Design and implement identity patterns including SSO, RBAC, privileged access, service identities, scopes/permissions access reviews, secrets, and least-privilege controls as applicable.
- Translate approved governance, security, privacy, legal, compliance, records, and data-management requirements into enforceable technical configurations and operating controls.
- Evaluate data flows, model/provider interactions, knowledge sources, connectors, and integrations to identify technical risks, dependencies, logging requirements, and control points.
- Establish monitoring, logging, alerting, auditability, usage reporting, cost/consumption visibility, licensing oversight, and operational performance metrics for AI services.
- Own or lead troubleshooting of complex platform, integration, authentication, authorization, data-access, agent, performance, and service-availability issues.
- Evaluate new AI products, models, platform capabilities, agents, and features and define technical testing, pilot, release, and support-readiness requirements.
- Develop and maintain technical architecture documentation, standards, configuration baselines, runbooks, support models, knowledge articles, and operational procedures.
- Define escalation paths and support boundaries for AI-related incidents and service requests and coordinate with vendors and internal technical teams as needed.
- Identify and implement automation opportunities that reduce manual administration and improve consistency, reliability, observability, and governance.
- Provide technical mentoring and guidance to AI Platform Engineers and other support resources while maintaining hands-on ownership of critical engineering work.
- Partner with cybersecurity, cloud/infrastructure, identity, application, data, architecture, service-management, procurement, legal/compliance, and business teams on AI service delivery.
Required Qualifications:
- Approximately 4 - 7+ years of professional experience in cloud engineering, systems engineering, platform engineering, DevOps, enterprise application administration, automation, security engineering, or comparable technical disciplines.
- Approximately 2+ years of meaningful experience with AI, intelligent automation, machine learning platforms, generative AI, or closely related enterprise technologies is preferred; equivalent depth demonstrated through hands-on delivery may be considered.
- Demonstrated experience engineering or operating complex enterprise cloud/SaaS platforms, integrations, identity controls, APIs, and support processes.
- Strong troubleshooting capability and experience leading technical resolution of multi-system issues involving applications, cloud services, identity, permissions, APIs, or data access.
- Ability to translate architectural, security, privacy, governance, or compliance requirements into practical technical designs and operational controls.
- Strong systems-thinking skills and the ability to understand how AI platforms interact with identity, data, applications, networks, cloud services, security controls, and operational processes.
- Ability to design practical service structures around rapidly changing technology without overengineering or losing operational supportability.
- Ability to evaluate trade-offs across platform capability, security, privacy, cost, user experience, maintainability, and compliance requirements.
- Strong technical documentation, standards development, and communication skills for both engineering and governance audiences.
- Ability to mentor engineers, provide technical direction, coordinate across teams, and maintain ownership through implementation and steady-state operations.
Preferred Qualifications:
- Hands-on experience with at least two of the following ecosystems: OpenAI/ChatGPT and OpenAI APIs; Microsof Copilot/Copilot Studio/Azure AI services; Google Gemini/Vertex AI/Google Cloud.
- Experience implementing enterprise AI agents, RAG/grounding, knowledge integration, enterprise search, tool/function calling, model routing, or API orchestration patterns.
- Experience with PowerShell, Python, REST APIs, JSON, CI/CD or infrastructure automation, low-code workflow tools, or comparable automation technologies.
- Experience with enterprise IAM, Microsoft Entra ID or comparable identity platforms, OAuth/OIDC/SAML, RBAC, privileged access, service principals/service accounts, secrets, and access reviews.
- Experience implementing security, privacy, data-loss prevention, logging/audit, records/data-handling, responsible AI, or AI governance requirements in technology platforms.
- Experience establishing IT service-management models including incident, request, change, problem, knowledge, service ownership, escalation, monitoring, and operational reporting.
- Experience operating technology in a regulated, government, large-enterprise, or other control-intensive environment.
Education Required:
- Bachelor's degree in Computer Science, Information Systems, Engineering, Cybersecurity, or a related technical discipline, or an equivalent combination of education and progressively responsible 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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