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About This Role
The AI Solutions Engineer is a hands\-on technical role focused on building, deploying, and supporting AI\-driven solutions, including AI and Digital Employee offerings, that address real\-world client and internal business needs.
Operating at the intersection of emerging AI technologies and practical implementation, this role is responsible for executing on defined solution architectures, developing proof\-of\-concept and production\-ready systems, and integrating AI capabilities into scalable, supportable environments.
This individual works closely with Innovation leadership, AI Architects, engineering, and customer\-facing teams to bring AI solutions to life. Responsibilities include building agent\-based systems, implementing automation workflows, supporting client engagements, and contributing to the delivery of commercially viable AI services.
ESSENTIAL FUNCTIONS:
- Develop and support AI\-based managed services and Digital Employee offerings from prototype through production deployment \- building and deploying AI Employees that perform real work across business functions, not demos or prototypes, but systems used in production.
- Engineer agentic systems with memory, tools, and orchestration, working alongside architects to bring advanced AI designs to life.
- Help define the future Digital Workforce platform, delivering solutions both internally at Marco and externally as client\-facing services.
- Build and implement AI\-driven solutions with a focus on LLMs, generative AI, AI agents, and automation platforms from leading providers.
- Execute on defined solution architectures by developing, integrating, and deploying AI systems in real\-world environments.
- Design and deliver proof\-of\-concept implementations to validate AI use cases, technical feasibility, and business value.
- Collaborate with AI Architects, Innovation, engineering, and operations teams to implement scalable, secure, and supportable solutions.
- Integrate AI solutions into client environments, including APIs, business systems, and automation platforms.
- Ensure AI solutions align with client business objectives, governance requirements, security best practices, and responsible AI principles.
- Support client engagements including technical discussions, solution implementation, pilots, and ongoing optimization.
- Translate complex AI concepts into clear, practical guidance for internal stakeholders and client audiences.
- Contribute to documentation, enablement materials, and internal training related to AI offerings.
- Continuously monitor emerging tools, frameworks, and best practices to improve solution delivery and execution.
- Participate in cross\-functional planning sessions to align AI initiatives with business strategy and client needs.
QUALIFICATIONS:
- Bachelor's degree in Computer Science, Information Technology, Data Science, or a closely related discipline.
- 3–6\+ years of experience in software engineering, solution implementation, automation, or related technical roles.
- Hands\-on experience building or implementing artificial intelligence, machine learning, generative AI, or large language model (LLM)\-based systems.
- Experience developing and integrating APIs, automation workflows, or distributed systems in production environments.
- Relevant certifications related to cloud platforms, AI/ML, or software engineering are preferred but not required.
REQUIRED SKILLS:
- Hands\-on experience building and deploying AI solutions, including large language models (LLMs), agent\-based systems, and automation workflows.
- Proficiency in software development and integration (e.g., Python, APIs, SDKs, or equivalent modern development tools).
- Experience working with cloud platforms and modern application architectures (cloud\-native or hybrid environments).
- Ability to implement and integrate AI solutions into business processes and existing technology ecosystems.
- Strong problem\-solving skills with the ability to move from concept to working solution quickly.
- Effective communication skills, with the ability to explain technical concepts to both technical and non\-technical audiences.
- Proven ability to collaborate across engineering, innovation, sales, and delivery teams.
- Demonstrated curiosity and commitment to continuous learning in a rapidly evolving AI landscape.
- Ability to balance rapid experimentation with building stable, supportable solutions.
Pay Range: $109,855 \- $175,768 annually
*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: $109,855 \- $175,768 annually
Salary Context
This $109K-$175K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($142K) sits 34% below the category median. Disclosed range: $109K to $175K.
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.
Marco Technologies AI Hiring
Marco Technologies has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $175K - $249K.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% 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 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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