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About This Role
Location
United States
Employment Type
Full time
Location Type
Remote
Department
All Cost CenterG&AIT
Compensation
- $94,150 – $135,000 • Offers Equity • Offers Bonus
Kong has different base pay ranges for different work locations within the United States and Canada, which allows us to pay employees competitively and consistently in different geographic markets. Compensation varies depending on a wide array of factors, including but not limited to specific candidate location, role, skill set and level of experience. Certain roles are eligible for additional rewards including sales incentives depending on the terms of the applicable plan and role. Benefits may vary depending on location. US based employees are typically offered access to healthcare benefits, a 401(k) plan, short and long term disability benefits, basic life and AD&D insurance, among others.
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If you don’t think you meet all of the criteria below but are still interested in the job, please apply. Nobody checks every box - we’re looking for candidates that are particularly strong in a few areas, and have some interest and capabilities in others.
About the Role:
Kong's IT team is standing up a new capability: a small, specialized group that goes directly into business functions and builds AI into how work gets done. We call them AI Enablement Specialists — and we're looking for a senior practitioner to help lead that effort.
This isn't an advisory role. You'll be hands-on with the teams you support, building tools that solve real problems and helping people develop new skills they'll carry forward. The scope is company-wide — Sales, Marketing, Finance, HR, Legal, Customer Success, and beyond. You'll have the autonomy to prioritize where your time creates the most value, and the backing of IT leadership to get things done.
If you've already done this kind of work — transforming how people and teams operate through AI — and you're looking for an environment where you can do it at scale with real ownership, this is the role.
What You'll Do:
- Spend time inside business teams to understand how they actually work — their tools, processes, pain points, and highest-priority outputs.
- Build AI-powered workflows, automations, and custom tools that address real bottlenecks and deliver measurable time savings.
- Help team members develop their own AI skills through one-on-one coaching, working sessions, and structured practice — moving them from early experimentation to confident, independent use.
- Recognize when a solution built for one team applies to others, and do the work to make it reusable across the organization.
- Establish a clear framework for tracking AI adoption progress across business units and report findings to IT leadership on a regular cadence.
- Set documentation standards for the team — every tool, workflow, and lesson learned should be captured so it doesn't live only in your head.
- Help develop Kong's internal AI governance standards in partnership with IT, Security, and Engineering.
- Mentor junior team members and interns, giving them hands-on experience and clear feedback.
- Stay current on AI developments and translate what's relevant into practical applications for Kong's business context.
What You'll Bring:
Minimum Requirements
- 5+ years of experience in a technical or cross-functional role that required problem-solving across teams and functions.
- A track record of building AI-powered tools, automations, or workflows that changed how real work gets done — with specific examples you can speak to.
- Experience helping others develop new technical skills, whether through formal training, one-on-one coaching, or team enablement programs.
- Clear, concise communication skills — you can explain a technical concept to someone who's never thought about it before, without talking down to them.
- Comfort with ambiguity and self-direction; you don't need a detailed roadmap to know where to start.
- Experience working in or with an IT organization, or a comparable role supporting business operations across multiple functions.
Preferred Qualifications:
- Hands-on experience with AI development platforms and tools such as Claude, Claude Code, MCPs, LLM APIs, custom agent frameworks, or workflow automation platforms.
- Background in change management, organizational development, or a large-scale rollout that required bringing skeptical stakeholders along.
- Experience in consulting, solutions engineering, or a client-facing technical role where you had to earn trust and deliver results quickly.
- Familiarity with the enterprise SaaS tools Kong runs on: Okta, Slack, Google Workspace, Salesforce, Jira, or Freshservice.
- Experience creating internal toolkits, playbooks, or training programs that outlived your involvement and were adopted at scale.
- Familiarity with Kong Konnect, Kong Gateway, or API management concepts — understanding how APIs and AI models connect will help you build smarter tools.
#LI-KH1
About Kong:
Kong Inc., the AI Connectivity Company, is building the connectivity layer of AI. Trusted by the Fortune 500® and AI-native startups alike, Kong’s unified API and AI platform enables organizations to secure, manage, accelerate, govern, and monetize the flow of intelligence across APIs and AI traffic — on any model, any cloud. For more information, visit www.konghq.com.
Compensation Range: $94,150 - $135,000
Salary Context
This $94K-$135K 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 KONG, 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. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($114K) sits 46% below the category median. Disclosed range: $94K to $135K.
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.
KONG AI Hiring
KONG has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span CO, US, US. Compensation range: $135K - $251K.
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
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