Interested in this AI/ML Engineer role at Waterfield Technologies?
Apply Now →Skills & Technologies
About This Role
Director, 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
Waterfield Tech is seeking a Director, AI & CX Services to lead and grow one or more professional services delivery practices that design, implement, and modernize customer experience and contact center solutions across leading CCaaS and UC platforms and our Applied AI capabilities. The Director owns the full P&L for the practices held and is accountable for delivery outcomes, quality, growth, and the people in each practice.
Bringing deep, hands-on delivery expertise, the Director sets the delivery approach, standards, and CX modernization roadmap for the practices held, is the senior escalation and executive point of contact for customers, owns the manufacturer/partner relationships, and partners across Sales, Product, and Operations. The role leads the technical leads and engineers who deliver the practices, including any custom development/Applied AI that extends the platforms.
Key Responsibilities
- Lead one or more AI & CX Services delivery practices end to end, accountable for delivery outcomes, quality, utilization, and margin across the practices held.
- Own the full P&L for the practices held (revenue, margin, cost structure, headcount investment, and partner tier economics) and own the capacity to pipeline strategy and the investment that closes the gap.
- Set and communicate the delivery strategy and CX modernization roadmap for the practices held: secure senior leadership buy-in and resources; monitor industry trends, partner roadmaps, and customer feedback; and lead organizational change (change management plans for new methodologies, technologies, and operating models) that fosters continuous improvement and innovation.
- Lead and develop the teams within the practices held: hire, coach, develop, and retain leaders and individual contributors; set goals, run performance and career development, and build leadership depth and succession.
- Grow the delivery capability and own the manufacturer/partner relationships for the practices held, including certifications, enablement, partner tier, roadmap alignment, and joint delivery.
- Establish portfolio level delivery governance and roadmap and release management standards for the practices held, including cadence, health reporting, KPIs, risk and escalation management, quality gates, release readiness, and the accelerator/IP release pipeline; report progress and outcomes to senior leadership.
- Set and own delivery standards, methodology, reference architectures, accelerators, templates, quality bars, and the implementation to support handoff and lifecycle standards for the practices held (flows/IVR, IVA/agent assist, WFO/WEM, reporting/administration, and CRM/CTI integrations); run post-mortems and lessons learned, and feed delivery insight into renewals and expansions.
- Own resource and capacity planning across the practices held; partner with Sales on scoping, feasibility, and level of effort while protecting margin during pre-sales.
- Serve as the senior escalation and executive point of contact for customers across the practices held; maintain executive relationships, satisfaction, and reference-ability.
- Partner cross-functionally with Sales, Product, IT, Operations, and commercial, pre-sales, and proposal colleagues, bridging technical and non-technical teams to drive aligned execution of CX modernization.
- Drive AI enablement in both directions: delivering AI solutions to customers and applying AI to delivery operations (portfolio health signals, status roll-ups, risk analysis, documentation) across the practices held.
- Where a custom development/Applied AI capability is held, own the team that builds integrations, APIs, conversational AI, and AI-enabled applications that extend the platforms, and align its roadmap to customer and practice needs.
- Review the customer satisfaction and delivery results (CSAT, NPS, and KPIs) for the practices held; use that data, customer feedback, and delivery metrics to identify pain points and trends and drive data driven decisions that refine delivery and the CX strategy.
- Ensure booking, time entry, and delivery operations within Microsoft Dynamics 365/Project Operations across the practices held.
How This Role Is Measured
- Practice P&L & economics: Gross margin and revenue vs. plan; utilization to target; cost structure vs. plan; estimate at completion/burn variance within threshold; headcount ROI.
- Delivery execution & quality: On time delivery and schedule variance across the practices held; reduction in escalations, rework, and margin leakage; quality gate/release readiness pass rate; estimate accuracy.
- Customer outcomes & satisfaction: CSAT/NPS trend across the practices held; executive relationship health and reference-ability; resolution and recovery of at-risk engagements.
- People & leadership: Team retention and engagement; certification and skills growth; leadership bench and succession depth; goal setting and performance review completion.
- Growth & capacity: Practice expansion and revenue growth vs. plan; pipeline conversion and capacity to pipeline coverage; demand forecast accuracy.
- Partner standing: Manufacturer certifications and partner tier maintained or improved; partner sourced pipeline and joint delivery results.
- Strategy, standards & AI enablement: Adoption of delivery standards, methodology, and accelerators; CX modernization roadmap and change initiative milestones delivered; AI in delivery adoption and impact.
- Operational discipline: Data accuracy, completeness, and timeliness of D365 booking, time entry, and delivery operations.
Primary Delivery & Technical Experience
- Strong Five9 practice experience required, with multiple production implementations and migrations at scale (inbound, outbound, blended, Intelligent Virtual Agent (IVA), Agent Assist, WFO/WEM, Studio/IVR, reporting and VCC administration, and CRM/CTI integrations such as Salesforce and ServiceNow).
- Zoom (Contact Center, Phone, and Workplace) and custom development/Applied AI delivery preferred.
- All other CCaaS/UC practice areas a plus: Genesys (Cloud CX), Cisco (Webex Contact Center/UC), Avaya (Aura/AEP/ACO), and Google (Contact Center AI/Dialogflow).
- AI enablement: Conversational AI, virtual agents, agent assist, real-time analytics, and AI-powered self-service.
- 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
- 12+ years delivering complex CX/contact center technology services, with 5+ years leading, building, and retaining delivery teams (people leadership) accountable for delivery outcomes and margin.
- Grounded in hands-on delivery, with career progression through implementation consultant, technical/solutions architect, or professional services/telephony (CCaaS) engineering into delivery leadership.
- Proven track record leading CX modernization initiatives at a leadership level.
- Demonstrated financial and practice economics acumen: margin, utilization, burn vs. plan, and estimate at completion.
- Experience owning manufacturer/partner relationships, including certification, enablement, and partner tier management, and leading customer executive governance and recovery of at-risk engagements.
- Software development/Applied AI engineering leadership a plus.
Leadership Attributes
- Executive presence and people leadership: build, coach, and retain multi-team technical organizations.
- Strong financial and operational discipline; balances growth, margin, and headcount investment while protecting delivery quality.
- Data driven, innovative, and future oriented; turns customer and delivery data into modern CX strategy.
- Sets strategy and standards across diverse teams and resolves cross-team tensions constructively.
- Outstanding communication across all levels, including C-suite and partner executives; strong change management expertise; thrives amid rapid change and ambiguity.
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/PgMP, ITIL, and change management credentials (e.g., Prosci ADKAR) a plus, but not required for this role.
- Availability to work flexible hours as needed, including senior customer 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 15% 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 $108K-$145K 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. Director-level AI roles across all categories have a median of $272,250. This role's midpoint ($126K) sits 40% below the category median. Disclosed range: $108K to $145K.
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
AI roles in Austin pay a median of $207,800 across 271 tracked positions. That's 3% below the national 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.