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About This Role
AI Foundry Engineer (US\- Remote)
What to expect when you join Tantus, a Sikich Subsidiary
Team members at Tantus, a Sikich Subsidiary have a lot in common while also being part of a rich and varied group of contributors, creating a distinct and thriving culture. Chief among our commonalities is a desire for growth and a shared unity of purpose in our professional lives. We believe that through diverse perspectives, challenging the status quo and rewarding action, we accelerate innovation and drive growth – for our clients, for ourselves and for our communities.
The professional services landscape continues to evolve. For Tantus, a Sikich Subsidiary, this means we have an opportunity to further cement our leadership position in this industry and continue to grow our organization in increasingly exciting ways. This growth is meaningful for every team member at our company because larger companies simply see more interesting client opportunities and can attract impressively talented individuals like you. Through a dedicated focus on key business priorities and intentionally creating a rewarding employee experience, Tantus, a Sikich Subsidiary has developed into a highly regarded provider of professional services and a sought\-after employer of choice.
Do you want to work with other skilled and agile practitioners who thrive on challenge and growth? We believe everyone has untapped potential. That’s why we lead with vision and act fast, pairing deep expertise with practical solutions. Our teams cut through complexity and deliver real impact. It's our hope that you find more than just a job. At Tantus, a Sikich Subsidiary, you'll find optimism, clear insights and genuine warmth, without egos.
*Are you ready to grow with us?*
Position summary
Tantus, a Sikich Subsidiary, is seeking a highly skilled AI Foundry Engineer to support our government client.
What will you do in this role?
- Understand and translate business requirements and problem statements into a set of prompts that direct AI foundry platforms and ecosystems to generate full stack, AI and agent\-based systems
- Assess the generated systems code to discover design gaps and inefficiencies, standard and coding style issues. Identify and implement fixes in the design and code as necessary
- Test, verify, and deploy the end\-to\-end system / application
What do you need to succeed in this role?
- Must be able to obtain and maintain a government issued Public Trust
- 20\+ years of overall software, distributed systems, and cloud native system development experience
- Coding standards (Google JavaScript / JSON style guide, PEP 8 style guide)
- Design patterns (GOF, Others)
- Architecture frameworks
- 2\-3\+ years of developing and integrating AI, Gen AI, and agentic AI algorithms, models, frameworks, and prompt engineering
- Knowledge about emerging standards for AI agent ecosystem
- SME / Power user for at least one Gen AI system (Gemini, ChatGPT, Claude, CoPilot)
- Prompt engineering \- translating business / user requirements into prompts
- 5\-7\+ years of in\-depth, hands\-on experience with one or more web application frameworks and programming languages
- Angular, React, Python, NodeJS, Rust
- Minimum of 5 years in\-depth, hands\-on experience with one or more cloud tech stacks, including virtual network architecture, design, implementation and management
- AWS / Google (preferred)
- AZURE / Oracle
- Minimum of one\-year in\-depth, hands\-on experience with one or more QA frameworks and test automation platforms
- Experience in testing AI and LLM’s using tools to evaluate accuracy, bias, and reliability of AI tools and systems
- Hands\-on experience in using tools such as Brainstrust and Galileo
In compliance with this state’s pay transparency laws, the salary range for this role is $165,000\-$195,000\. This is not a guarantee of compensation or salary, as final offer amount may vary based on factors including but not limited to experience and geographic location.
In addition, specific skills/experience required are as follows:
- Servant Leader – You are hyper focused on engaging employees, fostering their development, and building a positive culture.
- Solutions Focused – You see opportunities in every business problem and can develop, articulate, and implement solutions.
- Collaboration – You are a relationship builder across all levels of the organization and across all business units.
- Instills Trust \- You do what you say, and you follow through on commitments, you act with integrity, you are consistent and are perceived as credible.
- Impact \& Influence Thinking – You gain support for ideas, proposals, and solutions, and get others to act, with or without formal authority, to advance initiatives/objectives.
About Tantus, a Sikich Subsidiary
Tantus, a Sikich Subsidiary, offers the public and private sectors a diverse platform of professional services across consulting, technology, and compliance. Highly specialized and hands\-on teams deliver integrated solutions rooted in deep industry experience. Our approach is strategically and thoughtfully designed to help our clients, teams and communities accelerate success.
Tantus, a Sikich Subsidiary has approximately 2,000 team members and operates across North America, EMEA and APAC.
Tantus, a Sikich Subsidiary Total Rewards
Our team members enjoy expansive benefits ranging from competitive compensation and insurance options to wellness programs and a flexible time off policy, to name only a few. Tantus, a Sikich Subsidiary also takes pride in prioritizing team members’ health, total wellbeing and time spent with family, friends and in the pursuit of personal goals, hobbies, and endeavors.
Some examples of our many benefits:
- Tantus, a Sikich Subsidiary maintains a Flexible Time Off (FTO) Policy. We encourage every full\-time employee, as your role permits, to utilize paid time off (personal time, mental/physical health care, vacation, sick leave, etc.). Waiting for time off to accrue is common at other companies. At Tantus, a Sikich Subsidiary, you do not have to wait for this benefit to kick in. FTO is activated on your first day with our organization.
- Tantus, a Sikich Subsidiary will also recognize paid holidays during the year and strives to permit employees to have time off the last week of the calendar year when client and project work permits.
- Tantus, a Sikich Subsidiary offers a comprehensive wellness program to engage, challenge and empower team members to take responsibility for their wellbeing. Activities can be tracked through our wellness provider to obtain gift cards and other rewards.
We also offer:
- Flexible work arrangements
- Health, dental, vision, life, and accident/death/disability insurance options
- HSA employer contribution
- Eleven (11\) paid holidays annually.
- A robust paid Parental Bonding Leave program covering birth, adoption, and foster children.
- 401(k) with employer contributions
- Tuition reimbursement
- Generous employee referral bonus program
- Client referral bonus program
- Pet insurance
- FORCE – Tantus, a Sikich Subsidiary community volunteer program enabling each team member to use up to four hours of paid time annually to volunteer and make a difference in their local communities.
Want to learn more? Visit our Careers websiteorGlassdoor profile.
Tantus, a Sikich Subsidiary is an Equal Opportunity Employer M/F/D/V
*Sikich practices in an alternative practice structure in accordance with the AICPA Professional Code of Conduct and applicable law, regulations, and professional standards. Sikich CPA LLC is a licensed CPA firm that provides audit and attest services to its clients, and Sikich LLC and its subsidiaries provide tax and business advisory services to its clients. Sikich CPA LLC has a contractual arrangement with Sikich LLC under which Sikich LLC supports Sikich CPA LLC’s performance of its professional services. Sikich LLC and its subsidiaries are not licensed CPA firms.*
*“Sikich” is the brand name under which Sikich CPA LLC and Sikich LLC provide professional services.* *The entities under the Sikich brand are independently owned and are not liable for the services provided by any other entity providing services under the Sikich brand. The use of the terms “our company”, “we” and “us” and other similar terms denote the alternative practice structure of Sikich CPA LLC and Sikich LLC.*
Salary Context
This $165K-$195K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Sikich, 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($180K) sits 18% below the category median. Disclosed range: $165K to $195K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Sikich AI Hiring
Sikich has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US. Compensation range: $195K - $195K.
Location Context
AI roles in Austin pay a median of $214,343 across 87 tracked positions.
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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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