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About This Role
Company Overview
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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
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We are looking for a senior developer to join our Conversational AI Design \& Delivery team as a core technical contributor. You will work in collaboration with our technical lead to design, develop, test, and deliver conversational AI solutions for customers.
This is not a support role. We need someone who can operate as a technical pillar on the team: autonomous, reliable, and strong enough to carry delivery workstreams independently while collaborating closely with technical leadership.
What You’ll Do
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- Design, develop, test, and deliver conversational AI solutions (chatbots, voicebots, IVR systems) for customer and internal projects
- Take ownership of delivery workstreams end\-to\-end, from technical design through deployment
- Collaborate with cross\-functional teams — project managers, conversational designers, QA — across the full project lifecycle
- Work directly with customers throughout project delivery, including technical discussions and demos
- Apply disciplined software engineering practices, fully leveraging agentic coding tools (e.g. Claude Code, Codex) to maximize quality and velocity
Core Requirements
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- 6\+ years of hands\-on software development experience
- Proficiency across multiple programming languages, with 4\+ years using Python or TypeScript
- Experience with cloud platforms (GCP, AWS, or Azure)
- Demonstrated ability to work autonomously and carry technical delivery with minimal supervision
- Experience with disciplined engineering practices: version control, code review, CI/CD, automated testing
- Strong communication skills — comfortable interacting with customers, presenting technical work, and collaborating across disciplines
Key Qualities
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- Team player — collaborative, willing to pitch in where needed
- Creative problem solver — brings ideas, not just execution
- Organized and dependable — manages multiple workstreams without dropping balls
- Curious and adaptable — eager to learn new platforms and technologies
- Comfortable with ambiguity — can navigate evolving requirements and new problem domains
Nice To Have
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- Experience in delivering solutions for customer service / contact center (CCaaS) environments
- Exposure to NLU or speech technologies (e.g. speech recognition, speech synthesis)
- Experience working in a consulting or professional services delivery model
- Experience with Google Dialogflow, Amazon Lex, or similar conversational AI platforms
Physical Requirements
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- Ability to sit for extended periods
- Ability to lift or carry objects up to 10 lbs.
- Frequent use of computer, telephone and standard office equipment.
### 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.
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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% 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 $178,940 based on 11,900 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Waterfield Technologies AI Hiring
Waterfield Technologies has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Tulsa, OK, US.
Location Context
Across all AI roles, 16% (613 positions) offer remote work, while 3,187 require on-site attendance. Top AI hiring metros: New York (2,448 roles, $210,000 median); San Francisco (1,990 roles, $253,000 median); Los Angeles (1,686 roles, $189,000 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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, 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 $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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