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About This Role
About the position:: Velosio is looking for a AI Solution Architect to help turn AI product concepts and business use cases into practical, buildable solutions.
As a AI Solution Architect at Velosio, you will design how Microsoft AI services, agents, enterprise data, APIs, Dynamics 365 applications, and Velosio intellectual property work together. Working closely with Product Management, AI Developers, Dynamics architects, and delivery teams, you will help establish reusable technical patterns that support Business Central, Customer Engagement, and Finance & Operations. Who we are::
At Velosio, we focus on what matters most - our people. We are a values-driven organization committed to delivering an outstanding employee experience to all our team members.
Velosio’s years of experience with business applications translate to a deep bench of tenured team members with competency across enterprise resource management (ERP), customer relationship management (CRM), and cloud services.
We have earned recognition from Microsoft as a top 1% performing partner worldwide and an emerging NetSuite leader. We have strategic independent software vendor (ISV) partnerships and a portfolio of solution and service offerings to accelerate what’s next for business.
Our mission is to enable clients to realize business value faster, simplify the process of deploying technology, acquire deeper data-driven insights, and explore ongoing innovation to drive business forward. We support the entire Microsoft Dynamics portfolio, Office 365 family and Azure services. Velosio is the only Microsoft Cloud Distributor that specializes in Dynamics 365 and is a prominent Microsoft Master VAR. Headquartered in Atlanta, Georgia, Velosio’s 400 employees serve over 4,000 clients.
Beyond our expertise, as a Velosio team member, you can leverage the Velosio award-winning culture - a network of top-notch peers, day-to-day flexibility, career development resources and the largest incentive opportunities in the industry. Your day might look like::
- Translate product requirements and business use cases into technical AI solution designs
- Design AI agents and agent-driven workflows using Microsoft AI technologies
- Define how agents access data, call tools and APIs, interact with Dynamics 365, and complete actions safely
- Design retrieval-augmented generation (RAG), knowledge retrieval, context management, and Model Context Protocol (MCP) patterns
- Recommend when to use Microsoft first-party capabilities, custom development, or reusable Velosio IP
- Create common AI services and components that can be reused across Business Central, Customer Engagement, and Finance & Operations
- Define reference patterns for authentication, authorization, agent orchestration, APIs, logging, monitoring, testing, and deployment
- Build prototypes and proofs of concept to validate new technologies, architecture choices, and high-risk components
- Partner with AI Developers to convert architectural designs into working, supportable software
- Participate in technical design, code, and architecture reviews
- Work with BC, CE, and F&O specialists to connect AI capabilities to Dynamics 365 data, processes, and extensibility frameworks
- Design integrations using Business Central APIs and AL extensions, Dataverse and Power Platform, and F&O data entities, business events, and APIs
- Define clear boundaries between the shared AI platform, application-specific components, and product-specific business logic
- Ensure designs account for security roles, data access, client isolation, error handling, and human approval where appropriate
- Evaluate relevant capabilities across Microsoft Foundry, Azure OpenAI, Copilot Studio, Azure AI Search, Semantic Kernel, Microsoft Fabric, Dataverse, Power Platform, Azure Functions, API Management, Entra ID, GitHub, and Azure DevOps
- Create technical documentation, architecture diagrams, decision records, and development guidance for reusable AI patterns
What you'll bring::
- 3+ years of experience in solution architecture, software development, Azure engineering, AI engineering, enterprise application integration, or a related technical role
- Hands-on experience designing or developing generative AI, Copilot, agent, RAG, or LLM-based solutions
- Working knowledge of Azure OpenAI and several of the following: Microsoft Foundry, Copilot Studio, Azure AI Search, Semantic Kernel, MCP, Dataverse, Power Platform, or Microsoft Fabric
- Experience with C# and/or Python, REST APIs, JSON, cloud services, source control, and CI/CD practices
- Experience integrating enterprise applications, data sources, and services through APIs
- Ability to explain technical designs clearly to developers, product managers, Dynamics specialists, and business stakeholders
- Ability to work effectively in an emerging technology environment where requirements and architecture evolve quickly
You might also bring:* Experience with at least one Microsoft business application: Dynamics 365 Business Central, Dynamics 365 Customer Engagement/Dataverse, or Dynamics 365 Finance & Operations
- Experience with Microsoft Entra ID, Azure Functions, Azure API Management, GitHub, Azure DevOps, application monitoring, or automated testing
- Understanding of AI security, responsible AI, data governance, prompt injection risks, and human-in-the-loop controls
- Microsoft certifications such as AI-102, AZ-305, PL-600, or relevant Dynamics 365 credentials
- Experience working for a Microsoft partner, software company, consulting organization, or enterprise product team
How success will be measured:* AI product concepts are translated into clear, buildable architectures and development guidance
- Engineering teams have practical standards for agents, RAG, MCP, APIs, security, testing, monitoring, and deployment
- New Microsoft AI capabilities are evaluated quickly and adopted selectively based on business value and technical fit
- At least one priority AI capability progresses from architecture or prototype into working product functionality
Some reasons you might like working with us::
At Velosio, YOU MATTER.
Due to our ongoing commitment to delivering an exemplary employee experience, Velosio has been recognized by Comparably in multiple categories – including Best Company Culture (2021-2024), Best Company Outlook (2022-2025), Best Company for Career Growth and Best Leadership Teams (2022-2025).
Access the following link to see why:* 100% of current Velosio team members feel the company is invested in their career growth
- 99% feel their manager cares about them as a person
- 99% look forward to interacting with their coworkers every day
https://www.comparably.com/companies/velosio At Velosio, YOUR WELLNESS MATTERS.
We know one size doesn’t fit all, which is why we offer a comprehensive benefits package that allows our team members to create a personalized plan best suited for their unique needs, including:* 3 Medical Insurance options with a company contribution to HSA
- 3 Dental Insurance options including adult orthodontics
- 3 Vision Insurance options
- Unlimited PTO (UPTO)
- Remote or hybrid working environment based on location
- 401k Match 50% of the first 6%
- StayWell Program – a cash reimbursement up to $600 a year toward Wellness
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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Velosio, 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 $212,040 based on 13,780 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 $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Velosio AI Hiring
Velosio has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $188,200 across 2,430 positions. About 6% 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 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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