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About This Role
Overview:
*Join us as we Rise to the Challenge*
At KCI, we’re building an enduring community that provides unparalleled value to our employee\-owners. We make our mark designing and delivering our world\-class solutions, so we invest deeply in supporting and developing our team. We reward integrity and commitment, and when we do well, you do well. Our employee’s have the freedom to innovate, unlimited growth, a voice that matters, a lifestyle that works, and skin in the game. Achievements are shared and celebrated. As a team, we are motivated to better ourselves, each other, and the world around us.THE COMPANY
KCI Technologies, Inc. is a 100% employee\-owned engineering, consulting and construction firm serving clients throughout the United States. KCI is recognized as an industry leader, employing cutting\-edge technologies, management practices and strategic growth initiatives. Employee ownership fosters an entrepreneurial spirit, encourages technical expertise, and shapes strategic planning.
KCI is currently ranked \#50 on Engineering News\-Record’s list of the Top 500 design firms in the nation. KCI BENEFITS INFORMATION
We offer a competitive compensation package, family friendly benefits, a collaborative working environment, and the training, mentoring and resources you need to advance in your career.
We understand that you have choices, and we know that together we will make a great team!
*KCI is committed to building a diverse and inclusive staff, and we encourage women, people of color, LGBTQ\+ individuals, and individuals with disabilities to apply.*
KCI Technologies, Inc. is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability or veteran status.
Duties, Responsibilities \& Other:
As AI/ML Program Manager, you will serve as a trusted consultant guiding clients in the responsible and strategic adoption of AI/ML technologies and workflows. You will mentor AI/ML analysts and engineers while contributing directly to technical development and business strategy, demonstrating strong technical acumen, leadership skills, and a proactive approach to both project delivery and cross\-functional collaboration.
You will lead client engagements from discovery through delivery, designing tailored AI/ML programs, shaping policies around responsible AI use, and providing hands\-on technical insight into machine learning tools, data curation practices, and large language model (LLM) development. This position is ideal for someone at the intersection of technology strategy, applied AI/ML, and client consulting.Responsibilities* Serve as the primary advisor to clients on AI/ML adoption strategy, governance, and responsible use policies.
- Identify needs and opportunities to incorporate AI/ML for optimizing outcomes while supporting predictive modeling and data\-driven decision making for clients
- Lead needs assessments to identify organizational gaps, training requirements, and technical infrastructure needs.
- Guide clients to understand, trust, and embrace AI/ML as a viable, practical tool to drive business outcomes.
- Design and deliver AI/ML roadmaps, including implementation frameworks, program structures, and success metrics.
- Establish governance and compliance frameworks to support the safe and scalable deployment of AI solutions.
- Partner with client leadership to align AI initiatives with business goals and enterprise IT strategies.
- Define best practices and standards for scalable AI/ML operations within organizations.
- Demonstrate AI/ML capabilities through proofs\-of\-concept and success stories to build client confidence and accelerate adoption.
- Provide expertise in machine learning, and LLMs—guiding clients on model selection, training pipelines, and deployment practices.
- Implement and lead projects using retrieval\-augmented generation (RAG) systems, embeddings, and vector databases.
- Strategically utilize foundation models, including fine\-tuning, evaluation, and deployment.
- Advise on tools and platforms across the ML lifecycle.
- Lead project planning, define sprints, estimation, prioritization, and retrospectives.
- Collaborate internally to refine our consulting methodologies, solution frameworks, and service offerings in AI/ML.
- Mentor and guide junior developers in both technical and professional development.
- Help establish team skills baselines and contribute to training and upskilling initiatives.
- Conduct regular code and model reviews, establish best practices, and support SDLC.
- Provide updates to the Data Analytics Manager about project progress, team needs, junior analyst development needs, *etc*.
- Provide technical expertise during proposal development, including reviewing RFPs, crafting technical approaches, and estimating LOEs.
- Participate in client meetings and demos.
- Other duties as assigned.
Qualifications:
Requirements:* Bachelor’s degree in Computer Science, Computer Engineering, Systems Engineering, Data Science, or related field.
- 15\+ years of professional experience in client\-facing or consulting work.
- Strong experience with and understanding of ML frameworks and tools.
- Proficiency in Python and experience with additional languages (e.g., Java, R, C\+\+).
- Ability to translate complex AI/ML concepts into clear, actionable guidance for non\-technical stakeholders.
- Pre\-employment drug screening and background check are conditions of employment. Motor vehicle checks may be required based upon position.
Preferred Qualifications:* Master’s degree in Computer Science, Computer Engineering, Systems Engineering, Data Science, or related field.
- 8\+ years of professional experience in AI/ML.
- Prior experience designing or leading AI/ML training and upskilling programs.
- Expertise in data curation and preparation, including LLM training workflows and dataset development
- Experience with cloud platforms (Azure, AWS, GCP) and modern MLOps pipelines (Docker, Kubernetes, CI/CD).
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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At KCI 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
KCI Technologies AI Hiring
KCI Technologies has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US.
Location Context
AI roles in Austin pay a median of $215,300 across 523 tracked positions. That's 8% above 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, 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 $275,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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