Interested in this AI/ML Engineer role at Marco Technologies?
Apply Now →Skills & Technologies
About This Role
*Build the systems powering the next generation of AI workers.*
At Marco, we believe AI is bigger than chatbots and copilots. We're building AI\-powered Digital Employees—intelligent systems that automate work, interact across business platforms, orchestrate workflows, and become part of the modern workforce.
We're looking for an experienced AI Systems Architect to help design and build the platform foundations that make this possible.
This role sits at the intersection of AI architecture, software engineering, platform design, and emerging technology strategy. You'll work directly with modern AI ecosystems including Azure AI Foundry, Microsoft Fabric, Azure OpenAI, multi\-agent systems, APIs, orchestration technologies, and enterprise integration platforms to create scalable AI solutions that solve meaningful business challenges.
This is a role for someone who enjoys building systems—not simply configuring products.
What You'll Build
- You'll architect and deliver:
+ AI\-powered Digital Employees and intelligent workflow systems
+ Enterprise AI platforms leveraging large language models and agent\-based architectures
+ Multi\-agent systems capable of coordinating business processes and workflows
+ AI pipelines powered by Azure AI Foundry, Microsoft Fabric, and Azure AI services
+ Deep system\-to\-system integrations across SaaS, cloud, data, and business platforms
+ Secure APIs and communication layers enabling AI interoperability
+ Retrieval, orchestration, and context\-sharing frameworks
+ Scalable AI products and managed service offerings
+ Foundational architecture enabling Marco's AI Digital Workforce ecosystem
What You'll Do
- Architect intelligent systems and AI platforms
+ Design end\-to\-end AI architectures across cloud, hybrid, and enterprise environments
+ Build proof\-of\-concepts and production\-ready AI solutions leveraging generative AI ecosystems
+ Define reusable architecture patterns, standards, and technical frameworks
+ Create scalable designs for AI agents, orchestration workflows, retrieval systems, and enterprise integrations
+ Balance innovation, scalability, supportability, and operational excellence
+ Evaluate emerging technologies and rapidly determine practical business applications
- Lead AI platform and integration strategy
+ Design interfaces between AI systems and enterprise applications using APIs, service architectures, and event\-driven systems
+ Architect modern system\-to\-system integrations across Microsoft, SaaS, cloud, and line\-of\-business platforms
+ Establish standards for AI interoperability and orchestration
+ Develop patterns for context sharing, workflow coordination, and AI communication
+ Help define Marco's approach to emerging standards including Model Context Protocol (MCP), agent interoperability, and modern AI communication frameworks
+ Develop scalable architectural approaches for AI\-to\-system connectivity
- Help shape the future of AI Digital Employees
+ Lead development of AI and Digital Employee offerings from concept through market launch
+ Collaborate closely with engineering, innovation, security, sales, and operations teams
+ Participate directly in customer workshops, pilots, and technical strategy discussions
+ Translate highly technical concepts into practical business outcomes
+ Mentor internal teams on AI architecture and emerging technologies
+ Influence long\-term AI strategy and technical direction
What We're Looking For
- Experience
+ 8\+ years of experience in architecture, software engineering, platform development, or advanced technical leadership roles
+ Proven experience designing and delivering enterprise\-grade systems and complex technology solutions
+ Hands\-on experience with AI, generative AI, LLMs, machine learning systems, or intelligent automation platforms
+ Experience bringing concepts from proof\-of\-concept through production deployment
+ Demonstrated experience designing and implementing solutions using Azure AI Foundry and Microsoft Fabric within enterprise environments
- Core Technical Expertise
+ Deep hands\-on experience with:
- Azure AI Foundry
- Microsoft Fabric
- Azure OpenAI
- Azure AI Services
- Copilot ecosystem and extensibility
- Azure\-native AI architecture patterns
- Data grounding and enterprise AI architectures
- Building AI pipelines and workflows across Microsoft AI services
- AI deployment, governance, and operational best practices
- AI Systems \& Agent Architecture
+ Experience designing:
- Multi\-agent systems
- Agent orchestration frameworks
- Retrieval Augmented Generation (RAG)
- Context engineering and prompt orchestration
- Agent memory and workflow design patterns
- AI observability and governance
- Intelligent workflow systems and agent\-based architectures
- Platform Engineering \& System Integration
+ Deep expertise with:
- REST APIs and API\-first architectures
- System\-to\-system interfaces and enterprise integrations
- Event\-driven architectures
- SDK and connector development
- Middleware and integration frameworks
- Authentication systems including OAuth, SAML, and identity platforms
- Distributed systems and microservices
- Enterprise integration patterns
- Model Context Protocol (MCP)
- Agent communication standards and interoperability frameworks
- Development \& Platform Engineering
+ Hands\-on experience with:
- Python
- C\#
- JavaScript / TypeScript
- Software engineering fundamentals and application architecture
- Git and CI/CD workflows
- Cloud\-native application architecture
- Building production\-grade APIs and services
- Modern development and deployment practices
Pay Range: $151,504 \- $249,982 annually \+ 10% incentive opportunity
*The pay range listed for this position is based on candidate's skill level, experience, relevant licenses, and educational background. For detailed information about our benefits, please visit our careers page at www.marconet.com/careers.*
Location: This is a remote\-eligible position, however, Marco Technologies requires employees to reside within one of the following states: DE, FL, IA, IL, IN, KY, MD, MI, MN, MO, ME, NE, ND, NJ, PA, RI, SD, TX, WI
Compensation: $151,504 \- $249,982 annually
Salary Context
This $151K-$249K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Marco Technologies, 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($200K) sits 8% below the category median. Disclosed range: $151K to $249K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Marco Technologies AI Hiring
Marco Technologies has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $249K - $249K.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% of all AI roles offer remote work.
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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.