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About This Role
Manager, AI & CX Services
Customer Experience & AI Enablement
Company Overview
Waterfield Tech enables companies to select, design, and deploy tailored customer engagement solutions from the world’s leading providers enhanced by cutting edge Applied AI. The results are happier customers, enhanced insights, and lower costs.
Once live, we empower companies to support, optimize, and modernize those solutions and AI applications, leading to lower risk and increased efficiency. Our client-centric process and track record of success have earned the trust of clients around the world when it comes to customer interaction.
We value our people, their diversity, their dedication, and their commitment to customer satisfaction. We encourage each other. We understand the value of hard work and the importance of a healthy balance. We’re all on the same page… even though we may get there from different perspectives. All in all, it’s a pretty cool place to be and we’re growing our global team of engineers, sales professionals, and creative souls.
Position Summary
The Manager, AI & CX Services leads a professional services delivery team of CX and contact center engineers, delivering complex implementations and migrations across leading CCaaS and UC platforms, plus custom development and Applied AI. The Manager owns the team’s delivery margin, budget, and burn and is accountable for on time, on quality delivery across the full lifecycle.
Leading and developing the team day to day, the Manager reads the funnel to plan capacity, supports pre-sales scoping, and partners across Sales and commercial, pre-sales, and proposal colleagues. Depth in one or more contact center/CCaaS platforms is expected, with breadth across the ecosystem valued.
Key Responsibilities
- Manage a professional services delivery team of CX/contact center engineers to deliver complex implementations and migrations across leading CCaaS/UC platforms and custom development/Applied AI, often spanning multiple platforms and customer locations.
- Manage resources, schedules, and financials, including the delivery margin, budget, and burn for the team (or single team practice) held, and adhere to stage gate quality and control guidelines throughout the full delivery lifecycle.
- Lead, coach, and develop the team day to day: assign and balance work; build and motivate the team to meet goals, milestones, and responsibilities; and provide feedback, mentorship, and training.
- Manage issues, risks, change requests, dependencies, and critical path deliverables to ensure successful, on time delivery.
- Serve as the central point of contact and primary interface for project escalations; establish client and business relationships and manage customer expectations.
- Plan, document, and manage go-live events and related production changes, including go-live and immediate post go-live incident support, and availability for after hours cutovers, change windows, and urgent escalations as needed.
- Read the sales funnel and convert it into a forward capacity view for the team; flag under-utilization and hiring needs early.
- Support pre-sales: review proposal material, SOWs, and executed contracts to determine scope and required resource teams; contribute scoping, feasibility, and level of effort; and protect margin during pre-sales for the team (or single team practice) held.
- Provide status reporting and dashboards on milestones, deliverables, dependencies, risks, issues, and KPIs to leadership and stakeholders; analyze results and troubleshoot problem areas.
- Review the customer satisfaction and delivery results (CSAT, NPS, and delivery KPIs) for the team and use data driven insights to identify pain points, bottlenecks, and areas for improvement.
- Lead process improvement and help establish delivery practices, templates, tools, and standards, applying lean and agile methods to streamline workflows and drive continuous improvement, and conduct lessons learned reviews at project closure with after action reports.
- Ensure delivery meets Waterfield Tech standards and remains compliant with RFP, SOW, and contract terms; maintain booking, time entry, and delivery operations within Microsoft Dynamics 365/Project Operations.
- Collaborate with People & Culture to recruit, onboard, and retain talent and build a collaborative, inclusive team.
- Continue professional development to stay current on emerging technologies, methods, and platforms; perform other duties as assigned by leadership.
How This Role Is Measured
- Delivery execution & quality: On time milestone delivery and schedule variance across the team’s engagements; reduction in rework and escalations.
- Team economics: Delivered margin vs. plan; budget and burn vs. plan; utilization to target for the team held.
- Customer satisfaction: CSAT/NPS on delivered projects.
- Capacity & forecast: Forward capacity coverage vs. demand; demand forecast accuracy; early flagging of under-utilization and hiring need.
- Reporting & standards: Team adherence to established KPIs; timely, accurate reporting and dashboards; process improvement outcomes (cycle time, quality).
- People: Team retention, engagement, development, and certification/skills growth.
Primary Delivery & Technical Experience
- Deep, hands-on delivery expertise in one or more contact center/CCaaS practices is required; more than one is preferred: Five9, Genesys, Cisco (Webex Contact Center/UC), Avaya, Zoom, and Google (Contact Center AI/Dialogflow).
- AI enablement: Conversational AI, virtual agents, agent assist, real-time analytics, and AI-powered self-service.
- Custom development/Applied AI: Integrations, APIs, and AI-enabled applications that extend the contact center platforms.
- Multi-vendor/multi-platform delivery: Combining two or more of the above with CRM, WFM, QA, and analytics integrations; legacy to cloud migration delivery.
Qualifications
Experience
- 8+ years delivering technical professional services engagements (contact center/CCaaS implementations and migrations), including planning, resourcing, and execution, with progression into managing professional services delivery teams for contact center/CCaaS platforms.
- Grounded in hands-on delivery as an implementation consultant, technical/solutions architect, professional services delivery engineer, or telephony/contact center (CCaaS) engineer.
- Conversational AI design and NLU tuning experience, or experience leading a team with that expertise, required.
- Hands-on Dialogflow CX development experience, or experience leading a team with that expertise, required.
- Strong understanding of professional services delivery principles, methodologies, governance, and quality control, working with business stakeholders in a cross-functional matrix environment.
- Prior experience in a technical support or technical management role preferred.
Technical Proficiency
- Hands-on familiarity with one or more CCaaS/UC platforms and CX/CCaaS delivery.
- Microsoft Dynamics 365/Project Operations for booking, time entry, and delivery operations; delivery dashboards.
- AI-powered delivery and PPM tools (e.g., Microsoft Copilot, Claude, AI-enhanced PPM).
Soft Skills
- Team leadership: motivates and drives resources and stakeholders up, across, and down the organization.
- Excellent attention to detail; adapts to shifting priorities, competing demands, and timelines.
- Strong analytical and problem solving skills; manages multiple complex efforts simultaneously.
- Excellent oral and written communication and customer service skills across all levels of the organization.
- A passion for mentoring and developing team members; comfortable with ambiguity and constant change.
Education
- Bachelor’s Degree required; Master’s a plus. Emphasis in Business, IT, Engineering, or a related field preferred. Equivalent work experience will be considered.
Additional Requirements
- Manufacturer/platform certifications strongly valued across the CCaaS/UC ecosystem.
- PMP or an equivalent delivery leadership certification a plus.
- ITIL a plus.
- Availability to work flexible hours as needed, including after hours cutovers, change windows, and urgent escalations.
Physical Requirements
- Sitting: Ability to sit for extended periods (up to 80% of the time).
- Lifting: Ability to lift or carry objects up to 10 lbs.
- Equipment: Frequent use of computer, telephone, and standard office equipment.
- Travel: Ability to travel up to 5% for client meetings, training, or installations.
Waterfield Tech is proud to be an equal opportunity employer.
Waterfield Tech believes that all persons are entitled to equal employment opportunity and does not discriminate against its Employees or applicants because of race, color, religion, sex (including pregnancy), national origin, ancestry, age, marital status, citizenship status, disability, protected medical condition, military status, genetic information, or any other basis prohibited by applicable federal, state, or local law. This policy extends to all aspects of our employment practices including, but not limited to, recruiting, hiring, training, discipline, promotion, transfers, compensation, benefits, leaves of absence, termination, and all other terms and conditions of employment.
All employees must be citizens of the United States or be able to provide proof of employment eligibility.
Salary Context
This $100K-$125K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Waterfield Technologies, 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 $212,000 based on 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($112K) sits 47% below the category median. Disclosed range: $100K to $125K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Waterfield Technologies AI Hiring
Waterfield Technologies has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Tulsa, OK, US, US. Compensation range: $125K - $145K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 roles, $210,000 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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