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About This Role
Through passion and deep industry expertise, MCA Connect helps manufacturers succeed by unlocking innovation with actionable business insights. Our strategic solutions, innovation, and industry intelligence help manufacturers gain visibility, improve profitability, and achieve a competitive edge.
Established in 2002, MCA Connect has grown into one of the largest US\-based solution partners in Microsoft Business Applications and Azure Data \& AI / Digital \& App Innovation. Our Microsoft Specialties include Finance and Supply Chain, Analytics on Azure, Data Warehouse Migration, and Power Platform. We’re also a fifteen\-time Microsoft Partner of the Year and three\-time Inc. Best Workplaces award winner. Title Data \& AI Solution Architect
Location Virtual, home based office with travel (20%)
Description
The Data \& AI Solution Architect is responsible for being a partner to team members, a mentor, and directly enabling growth of the Data \& AI Services team at MCA. The Solution Architect is expected to support pre\-sales efforts, lead project implementations, drive innovation, upskill and mentor junior teammates, and partner with leadership to drive continuous improvement.
Within a project framework, the Data \& AI Solution Architect is responsible for building client relationships, leading delivery teams, effectively utilizing Agile project delivery methodologies, designing scalable and efficient technical architectures to satisfy client requirements, and performing hands on implementations of Microsoft data solutions for MCA Connect’s current and future customers/projects.
### Responsibilities
- Work across Microsoft technologies in Azure and On\-Prem to design, develop and deliver solutions that satisfy client requirements.
- Design and document scalable, secure, and efficient data integration and analytics architectures.
- Perform complex organization wide analysis and create a Data Strategy to support client specific needs.
- Effectively communicate the data strategy, implementation plan, and associated costs.
- Mentor's junior resources.
- Leads, designs, and implements DevOps and CI/CD solutions.
- Owns the project backlog, sprint planning, and task estimations.
- Responsible for ensuring projects stay on track, within budget, and that the development team is supported.
- Demonstrates the ability to be a key decision maker and strategist.
- Understands Data Governance techniques and MCA “Go To Market” Technologies \& common Architecture Designs.
- Obtain professional certifications to support our partner designations.
- Oversee project team and source code.
- Understands technology options for batch, streaming, and application integrations
- Responsible for designing and creating data models that align with the organization's needs and goals.
- Supports technical pre\-sales conversations, requirements, and estimations.
- Understands Master Data Management approaches and technologies.
- Defines project scope, timelines, and resource requirements.
- Must be open to \~10% travel on an as needed basis.
### Required Qualifications
- 7\+ years Data Warehousing (Azure/SQL/Synapse/Spark/Delta Lake), ETL (ADF Pipelines/SSIS), Analysis Services, DAX, Power BI Pro and Premium and/or equivalent.
- 2\+ years working in a Data Architect capacity.
- Familiarity with big data and event driven technologies like Hadoop, Spark, Kafka, Event Hubs, and Service Bus architectures.
- High level understanding of various Azure cloud data platforms and services.
- Demonstrated experience in designing and implementing data solutions for complex business problems.
- Experience working on projects involving large volumes of data, preferably in the Manufacturing industry.
- Experience migrating data processing workloads from on\-premises to Azure cloud technologies.
- Demonstrated the ability to communicate effectively with technical teams, business stakeholders, and other relevant parties.
- Bachelor’s degree in computer science or equivalent combination of education and experience preferable.
- Knowledge of Microsoft ERP and CRM tools desirable.
- Kimball Dimensional Modeling and batch/incremental ETL development approaches.
- Experience with database performance tuning activities.
- Experience developing and managing data quality monitoring and an understanding of data governance methodologies.
- Excellent communication skills both written and verbal.
Additional annual supplemental compensation $20,000 \- 30,000 (paid quarterly)Why work for MCA Connect?
Our compensation plan offers one of the best bonus structures in the industry. Along with this we also offer a generous benefit package:* Work/Life Balance with Unlimited Paid Time Off (UPTO)
- 401k Plan with Company Matching Contribution
- Monthly Stipend for Home Office Expenses
- Subsidized Medical, Dental and Vision Coverage
- Health Savings and Flexible Spending Accounts
- Company Paid Life and Disability Insurance
- Training, Certification and Continuing Education Support
MCA Connect offers limitless opportunities for personal and professional growth in a stimulating, challenging, and performance\-oriented work culture where you can share your ideas and make impactful daily contributions. Our employees are highly motivated and talented individuals dedicated to developing, marketing, and selling products designed to deliver value for mid\-market and enterprise\-size manufacturing, distribution, and energy companies. We take the time to train our consultants so that they understand the industries we serve and can deliver best practices, proven methodologies, and ongoing industry expertise to our clients.
MCA Connect is an Equal Opportunity Employer. MCA Connect promotes equal employment opportunity to all employees and applicants and does not discriminate on the basis of race, religion, color, creed, national origin, sex, age, sexual/gender orientation, status as a protected disabled or Vietnam Era Veteran, disability, or any other legally protected status. We firmly believe our differences make us stronger!
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
Salary Context
This $120K-$170K range is below 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 MCAConnect, 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 ($145K) sits 34% below the category median. Disclosed range: $120K to $170K.
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.
MCAConnect AI Hiring
MCAConnect has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $145K - $170K.
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
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