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Modern Work and AI Solutions Practice Lead
Winnipeg, MB, Canada
Convergence Networks is one of North America’s leading managed service and managed security providers. We are a service company focused on helping Clients leverage technology as a strategic tool and proactively protecting their business. We are fueled by providing outstanding service and sharing our passion for innovative technology as part of our integrated solutions. We deliver managed IT services, professional services, and business transformation programs across North America, empowering clients to thrive in a cloud\-first, AI\-enabled world.
Our Intelligent Solutions and Modern Work program is evolving to focus on Power Platform and Microsoft Copilot/AI as core service pillars. We’re looking for a senior Practice Lead to guide this evolution from our Winnipeg office.POSITION SUMMARY
The Modern Work Practice Lead is a senior, hands\-on leadership role responsible for shaping the direction and maturity of our Modern Work services. You will provide technical leadership, mentor and guide consultants, support project scoping and delivery, and contribute to pre\-sales and client conversations. This role combines practice leadership, technical depth, and people development, with a strong connection to real client work.WHAT YOU'LL DO* Define and evolve Modern Work offerings focused on Power Platform, Microsoft Fabric, and Copilot/AI.
- Provide day\-to\-day leadership, mentorship, and guidance to the Modern Work team.
- Set standards and best practices for low\-code development, automation, and AI adoption.
- Support project scoping, estimation, and solution design.
- Participate in key engagements as a senior architect or escalation point.
- Partner with Sales on discovery, solution shaping, and proposals.
- Stay current on Microsoft’s roadmap and translate it into client\-ready services.
WHAT YOU BRING* 5\+ years of experience in Microsoft 365, Modern Work, or digital workplace consulting.
- Strong hands\-on experience with Power Platform, Microsoft Fabric, and Copilot Studio.
- Solid understanding of Automation, AI implementation, and responsible AI adoption and governance practices.
- Experience designing governed, scalable low\-code and automation solutions.
- Demonstrated ability to mentor team members and provide technical leadership.
- Comfortable engaging clients and supporting sales conversations.
- Experience with practice development, service design, or solution ownership is an asset.
WHAT IS THE WORK ENVIRONMENT LIKE?* Normal office working conditions. Work requires regular sitting/standing at a desk, working with a computer. This position requires standing, walking, sitting, using hands, seeing, reaching, talking, writing, and hearing; it may require occasionally carrying or lifting equipment (10\-50 pounds) if working on\-site.
- Position may require hours that exceed normal working hours per day during peak periods; on\-call or travel work may include nights or weekends
- Position requires regular contact with others \- in meetings, by phone or by email.
- Interactions focus on data collection, problem solving, needs analysis and technical training development. Interactions are initiated in person or electronically.
- Position may require some travel to Convergence or client sites.
WHY SHOULD YOU WORK HERE?* Culture of unity, transparency, and trust. Our leadership team wants you to be successful at Convergence, and we will do anything we can to support your personal and professional growth.
- Group benefits plans including medical, dental, vision, and 401k.
- Education and certification reimbursement is also available so we can help you grow.
- We believe feedback makes us better. You can expect regular meetings with your manager and quarterly conversations about your performance and growth.
- Outstanding teammates. We’re very selective to make sure we have the best staff available for you to work alongside!
- Many teambuilding and company events throughout the year so you can get to know your teammates on a more personal level, as well as kick back and have some fun (families are oftentimes included as w
PERFECT FIT....
If this sounds like your type of place and you can wow us with your spectacular skill set, then we would love to hear from you!
We are an equal opportunity employer and invite diversity in our applicants; our differences make us stronger! We welcome and encourage applications from qualified candidates of all races, sexes, colors, religions, sexual orientations, disabilities, ages, and gender identities. Accommodations are available upon request for candidates taking part in all stages of the selection process.
The compensation range for this position is $104,000 \- $115,000, which includes base salary and variable compensation. Individual compensation is determined by a combination of factors including skills, experience, qualifications, and geographic location. In addition to base salary, eligible employees may have opportunities to participate in variable incentive programs designed to reward individual and organizational performance. We are committed to pay transparency and comply with all applicable pay transparency legislation.
Salary Context
This $104K-$115K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Convergence Networks, 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 $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($109K) sits 40% below the category median. Disclosed range: $104K to $115K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Convergence Networks AI Hiring
Convergence Networks has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Lansing, MI, US. Compensation range: $115K - $115K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 median).
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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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