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About This Role
About us
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We are the leading provider of professional services to the middle market globally, our purpose is to instill confidence in a world of change, empowering our clients and people to realize their full potential. Our exceptional people are the key to our unrivaled, culture and talent experience and our ability to be compelling to our clients. You’ll find an environment that inspires and empowers you to thrive both personally and professionally. There’s no one like you and that’s why there’s nowhere like RSM.
Manager \- MCI Contact Center AI \& Copilot
Contact Center AI \& Copilot Engineer \- Voice / CCaaS
The role is a client\-facing position focused on designing, building, and improving modern contact center experiences using AI, voice, and CCaaS technologies. This includes Copilot Studio, Dynamics 365 Contact Center, Genesys Cloud, Cisco Webex Contact Center, and related CRM and backend integrations. You’ll work closely with architects and delivery teams to create scalable, secure, and practical solutions that improve both customer and agent experiences.
You’re expected to think critically, ask questions, and figure things out. You’ll solve real business problems across voice, chat, and digital channels, translate business needs into technical solutions, and challenge assumptions when needed. This role is ideal for someone who is curious, positive, hands\-on, and motivated to keep learning as AI\-first contact center technology evolves.
Responsibilities
- Build and deploy AI agents using Copilot Studio for chat and voice experiences.
- Design call flows, IVRs, queues, and intelligent routing experiences across modern contact center platforms.
- Support pursuit and solution development activities by providing technical input, estimates, demos, and subject matter perspective for contact center AI and Copilot opportunities.
- Configure and support contact center solutions using Dynamics 365 Contact Center, Genesys Cloud, Cisco Webex Contact Center, and related CCaaS platforms.
- Integrate contact center platforms with CRM systems, backend applications, APIs, and data sources.
- Troubleshoot complex issues across voice, chat, email, SMS, and digital channels.
- Improve agent and customer experiences using automation, AI assist, knowledge access, and workflow optimization.
- Translate business needs into technical solutions and challenge requirements when a simpler or better approach is available.
- Test voice, routing, agent assist, and omnichannel experiences from an end\-user and support perspective.
- Validate application behavior, permissions, data access, and integration performance across environments.
- Create and maintain documentation, including call flow designs, configuration notes, test scripts, support guides, and solution overviews.
- Continuously test, learn, and optimize solutions based on real usage, client feedback, and platform capabilities.
Required Qualifications
- 4\+ years of experience supporting contact center, CRM, Microsoft 365, Power Platform, or enterprise technology solutions.
- Hands\-on experience with one or more contact center platforms, such as Dynamics 365 Contact Center, Genesys Cloud, or Cisco Webex Contact Center.
- Experience designing or supporting IVRs, call flows, routing logic, queues, escalation paths, or omnichannel service experiences.
- Working knowledge of voice/telephony concepts such as SIP, call routing, numbers, queues, IVR, and agent availability.
- Experience translating business requirements into technical designs, configurations, test plans, or implementation tasks.
- Strong client\-facing communication skills and ability to work across business, technical, and delivery teams.
- Ability to troubleshoot complex issues across systems, integrations, permissions, and user experience layers.
Preferred Qualifications
- Experience with Copilot Studio, conversational AI, AI agents, or voice\-enabled automation.
- Experience with Power Platform, including Power Automate and Dataverse.
- Experience integrating CRM, contact center, and backend systems through APIs or middleware.
- Familiarity with Azure services, identity, security, permissions, and data access concepts.
- Experience with reporting, analytics, quality management, or workforce optimization in contact center environments.
- Experience in consulting, managed services, or client delivery environments.
At RSM, we offer a competitive benefits and compensation package for all our people. We offer flexibility in your schedule, empowering you to balance life’s demands, while also maintaining your ability to serve clients. Learn more about our total rewards at https://rsmus.com/careers/working\-at\-rsm/benefits.
All applicants will receive consideration for employment as RSM does not tolerate discrimination and/or harassment based on race; color; creed; sincerely held religious beliefs, practices or observances; sex (including pregnancy or disabilities related to nursing); gender; sexual orientation; HIV Status; national origin; ancestry; familial or marital status; age; physical or mental disability; citizenship; political affiliation; medical condition (including family and medical leave); domestic violence victim status; past, current or prospective service in the US uniformed service; US Military/Veteran status; pre\-disposing genetic characteristics or any other characteristic protected under applicable federal, state or local law.
Accommodation for applicants with disabilities is available upon request in connection with the recruitment process and/or employment/partnership. RSM is committed to providing equal opportunity and reasonable accommodation for people with disabilities. If you require a reasonable accommodation to complete an application, interview, or otherwise participate in the recruiting process, please call us at 800\-274\-3978 or send us an email at [email protected].
RSM does not intend to hire entry level candidates who will require sponsorship now OR in the future (i.e. F\-1 visa holders). If you are a recent U.S. college / university graduate possessing 1\-2 years of progressive and relevant work experience in a same or similar role to the one for which you are applying, excluding internships, you may be eligible for hire as an experienced associate.
RSM will consider for employment qualified applicants with arrest or conviction records. For those living in California or applying to a position in California
At RSM, an employee’s pay at any point in their career is intended to reflect their experiences, performance, and skills for their current role. The salary range (or starting rate for interns and associates) for this role represents numerous factors considered in the hiring decisions including, but not limited to, education, skills, work experience, certifications, location, etc. As such, pay for the successful candidate(s) could fall anywhere within the stated range.
Compensation Range: $107,000 \- $214,500
Individuals selected for this role will be eligible for a discretionary bonus based on firm and individual performance.
Salary Context
This $107K-$214K 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 RSM, 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 ($160K) sits 25% below the category median. Disclosed range: $107K to $214K.
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.
RSM AI Hiring
RSM has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Dallas, TX, US. Compensation range: $214K - $214K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 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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