Interested in this AI/ML Engineer role at IncWorx Consulting?
Apply Now →About This Role
Microsoft Solutions Consultant (AI, Power Platform & Microsoft 365) Are you the type of person who sees a business problem and immediately starts thinking about how to solve it?
Do you enjoy working directly with clients, leading discussions, building solutions, and taking ownership of outcomes from discovery through deployment?
IncWorx is looking for a highly motivated consultant who can independently lead client engagements while delivering innovative solutions across the Microsoft ecosystem. This role is ideal for someone who combines technical expertise with consulting, leadership, and problem-solving skills.
We're not looking for someone who waits for detailed instructions.
We're looking for someone who can ask the right questions, uncover business challenges, guide stakeholders toward decisions, and deliver solutions that create measurable value.
If, after reading below, this sounds like your dream role and organization, please start the process by completing our Culture Index survey here:
Complete survey to get started!
Location: Remote
Employment Type: Full-Time
Experience Level: Mid-Level to Senior
What You’ll Do
Lead Client Engagements* Facilitate discovery workshops and requirements gathering sessions
- Translate business challenges into practical Microsoft-based solutions
- Lead client meetings and executive discussions
- Build trusted relationships with stakeholders at all organizational levels
- Drive decisions, maintain momentum, and remove project roadblocks
- Communicate project status, risks, and recommendations proactively
Deliver Innovative Solutions* Design and implement solutions utilizing: + SharePoint Online + Microsoft Teams + Power Apps + Power Automate + Microsoft Copilot + AI-enabled solutions + Other Microsoft 365 technologies
- Apply business process improvement techniques to streamline operations
- Recommend creative approaches that improve outcomes, efficiency, and adoption
- Continuously identify opportunities to leverage emerging technologies and AI
Own Project Success* Lead engagements from discovery through deployment
- Manage priorities, timelines, and deliverables
- Identify and mitigate project risks
- Ensure projects remain aligned to client objectives
- Escalate issues appropriately while maintaining ownership
- Deliver high-quality work products with minimal oversight
Mentor and Lead Delivery Teams* Coordinate junior consultants and delivery resources
- Assign tasks and responsibilities across project teams
- Review deliverables and provide guidance
- Foster accountability, collaboration, and continuous improvement
What We’re Looking For
Required Characteristics* You are naturally curious and enjoy solving difficult problems
- You thrive in ambiguity and can create structure when none exists
- You take ownership of outcomes rather than waiting for direction
- You communicate clearly and confidently with both technical and business audiences
- You can independently drive projects forward without constant management oversight
- You are comfortable learning technologies you have not previously worked with
- You embrace change and continually seek better ways to deliver value
Technical Experience Experience with some or all of the following:
- Microsoft 365
- SharePoint Online
- Microsoft Teams
- Power Platform
- Power Apps
- Power Automate
- Microsoft Copilot
- AI-assisted development tools
- Microsoft cloud technologies
You do not need to be an expert in every technology listed. We place a high value on problem-solving ability, adaptability, and the willingness to learn.
- What Success Looks Like
After your first year, you are independently leading complex client engagements and successfully delivering solutions that create measurable business value.
You can:
- Lead a 500+ hour Microsoft or AI-focused project from discovery through deployment
- Manage client relationships with minimal delivery management intervention
- Drive executive conversations and project decisions
- Coordinate delivery teams and mentor junior consultants
- Consistently deliver high-quality work products
- Identify innovative approaches that improve delivery efficiency and client outcomes
✅ You May Not Be a Fit If... This role may not be the right fit if you prefer:
- Waiting for detailed instructions before taking action
- Working only on assigned technical tasks
- Having others manage project coordination and follow-up
- Avoiding difficult client conversations
- Focusing exclusively on technology rather than business outcomes
- Escalating routine decisions that you could own yourself
We're looking for consultants who embrace accountability, lead through ambiguity, and take ownership of client success.
Why Join IncWorx?* Work with a high-performing, values-driven consulting team
- Deliver meaningful business solutions that create measurable impact
- Help shape the future of AI-powered consulting and delivery
- Collaborate with talented professionals who value innovation and continuous improvement
- Work remotely while maintaining strong team connections
- Grow your skills across consulting, leadership, AI, and modern Microsoft technologies
Ready to Take Ownership? If you're energized by solving complex problems, leading client engagements, and building innovative solutions that drive real business outcomes, we'd love to hear from you.
Complete our Culture Index survey to get started: Complete survey to get started!
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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 IncWorx Consulting, 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 in Demand for This Role
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.
IncWorx Consulting AI Hiring
IncWorx Consulting 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 $188,000 across 2,316 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 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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