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About This Role
Your Job Koch Technology Data Solutions (KTDS) is seeking a Data & AI Solutions Architect to shape the future of data, analytics, automation, and AI across Koch.
In this role, you will partner with business leaders, engineers, product teams, and subject matter experts to solve complex business challenges. You will translate business outcomes into scalable solutions, guide technology decisions, establish reusable patterns, and accelerate delivery through modern Data & AI architectures.
The ideal candidate is an AI-native architect who uses AI as both a solution capability and a core part of how they work. You rapidly explore options, validate ideas through prototypes, and determine where AI, automation, software, data products, or process improvements create the greatest value.
Our Team
KT Data Solutions builds and operates reusable data, analytics, and AI capabilities that help teams make better decisions, automate work, and unlock new opportunities across Koch. We operate in a data-centric way-treating trusted, governed, and reusable data products as shared enterprise assets that provide the context for analytics, automation, and AI.
Our vision is to compound value through trusted data, reusable platforms, and AI-enabled solutions that scale across the enterprise.
Location: This role can be located in Wichita, KS / Atlanta, GA / Plano, TX and requires an in-office presence with flexibility.
This role is not eligible for VISA sponsorship.
What You Will Do
- Partner with stakeholders to understand business outcomes and translate them into scalable Data & AI architectures.
- Develop solution designs, architecture standards, and implementation guidance that promote consistency, reuse, and delivery speed.
- Build alignment and influence decisions across business, product, engineering, security, governance, and platform teams.
- Design end-to-end data and AI solutions spanning data platforms, integrations, analytics, automation, governance, and AI capabilities.
- Apply cloud-native, API-first, event-driven, and domain-oriented design principles.
- Identify opportunities where AI can improve decision-making, productivity, operations, and business outcomes.
- Establish architecture patterns, standards, and guardrails that enable secure, scalable, resilient, and supportable solutions.
- Apply Harness Engineering principles to embed governance and standards into delivery workflows.
- Leverage AI and AI-DLC practices to accelerate solution exploration, architecture, planning, documentation, and delivery while maintaining ownership of recommendations.
- Build and evaluate prototypes, reference implementations, agentic workflows, and technical spikes to validate approaches and reduce risk.
- Champion responsible AI practices, including security, privacy, governance, observability, evaluation, and human oversight.
Who You Are (Basic Qualifications)
- Experience translating business outcomes into scalable Data & AI architectures and influencing technical direction across teams.
- Experience designing end-to-end Data & AI solutions using cloud, data, and AI platforms such as AWS, Snowflake, Microsoft Fabric, or similar technologies.
- Experience advancing platform adoption through product management, developer enablement, or adoption metrics.
- Experience using AI and AI-DLC practices to accelerate solution design, planning, delivery, and decision-making.
- Demonstrated ability to create architecture artifacts and build prototypes, reference implementations, or technical evaluations that validate decisions and reduce delivery risk.
- Demonstrated understanding of architecture and responsible AI principles, including security, privacy, governance, observability, resiliency, and supportability; paired with strong stakeholder management and influence skills.
What Will Put You Ahead
- Experience defining and scaling enterprise Data & AI platform strategies, operating models, and shared capabilities across multiple domains.
- Experience applying modern architecture principles, including cloud-native, API-first, event-driven, and domain-oriented design, as well as AI-enabled workflows, generative AI, agentic AI, and RAG solutions.
- Experience using ontologies, knowledge graphs, semantic layers, or knowledge-based architectures.
- Experience scaling AI-assisted engineering practices that improve productivity, quality, reliability, or delivery speed.
- Hands-on experience building or operating Model Context Protocol (MCP) servers or similar standards for connecting AI agents to enterprise systems.
- Experience mentoring architects and technical resources to improve architectural decision-making and consistency.
At Koch companies, we are entrepreneurs. This means we openly challenge the status quo, find new ways to create value and get rewarded for our individual contributions. Any compensation range provided for a role is an estimate determined by available market data. The actual amount may be higher or lower than the range provided considering each candidate's knowledge, skills, abilities, and geographic location. If you have questions, please speak to your recruiter about the flexibility and detail of our compensation philosophy.
Hiring Philosophy
All Koch companies value diversity of thought, perspectives, aptitudes, experiences, and backgrounds. We are Military Ready and Second Chance employers. Learn more about our hiring philosophy here .
Who We Are
Koch creates and innovates a wide spectrum of products and services that make life better. Our work spans a vast number of industries across the world, including engineered technology, refining, chemicals and polymers, pulp and paper, glass, electronics and many more. Headquartered in Wichita, Kansas, Koch employs about 120,000 employees across the globe.
At Koch, employees are empowered to do what they do best to make life better. Learn how our business philosophy helps employees unleash their potential while creating value for themselves and the company.
Our Benefits
Our goal is for each employee, and their families, to live fulfilling and healthy lives. We provide essential resources and support to build and maintain physical, financial, and emotional strength - focusing on overall wellbeing so you can focus on what matters most. Our benefits plan includes - medical, dental, vision, flexible spending and health savings accounts, life insurance, ADD, disability, retirement, paid vacation/time off, educational assistance, and may also include infertility assistance, paid parental leave and adoption assistance. Specific eligibility criteria is set by the applicable Summary Plan Description, policy or guideline and benefits may vary by geographic region. If you have questions on what benefits apply to you, please speak to your recruiter.
Additionally, everyone has individual work and personal needs. We seek to enable the best work environment that helps you and the business work together to produce superior results.
Equal Opportunities
Equal Opportunity Employer, including disability and protected veteran status. Except where prohibited by state law, some offers of employment are conditioned upon successfully passing a drug test. This employer uses E-Verify. Please click here for additional information. (For Illinois E-Verify information click here , aquí , or tu ).
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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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Koch, 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 11,857 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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: $272,275; VP: $235,000.
Koch AI Hiring
Koch has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Atlanta, GA, US.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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