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About This Role
POSITION SUMMARY
McCarthy is seeking a full\-time AI Engineer II who will be part of the Engineering and Intelligence team. In this role, you will design, build, and support AI\-powered applications and workflows that help drive business value across the enterprise.
Working closely with the AI Manager, business stakeholders, and fellow engineers, you will contribute to the development of production\-grade AI solutions, including generative AI, agentic workflows, advanced analytics, and enterprise integrations. This is a hands\-on engineering role focused on implementation, experimentation, deployment, and continuous improvement of AI capabilities.
The ideal candidate combines strong software engineering fundamentals with experience in AI and data\-driven applications. Experience working within Palantir Foundry, including ontology\-driven development, data products, operational workflows, and AI\-enabled solutions, is strongly preferred.
RESPONSIBILITES
- Design, develop, test, and maintain AI\-powered applications, services, and workflows that support enterprise business processes.
- Build and support generative AI and agentic solutions using modern AI engineering practices and frameworks.
- Develop and maintain data pipelines, integrations, and reusable data products that enable AI and analytics use cases.
- Contribute to the design and evolution of data models and ontologies that support operational and analytical workflows.
- Develop and optimize prompts, retrieval strategies, and agent workflows to improve solution effectiveness, reliability, and user experience.
- Leverage AI\-assisted development tools and coding copilots to accelerate delivery while maintaining high standards for code quality, testing, security, and maintainability.
- Participate in MLOps and PromptOps processes, including deployment, monitoring, evaluation, versioning, and continuous improvement of AI systems.
- Collaborate with business and technical stakeholders to translate business requirements into practical technical solutions.
- Support production AI solutions through troubleshooting, performance tuning, monitoring, and ongoing enhancement activities.
- Contribute to documentation, reusable patterns, and engineering best practices that improve team effectiveness and solution consistency.
- Follow established AI governance, security, and Responsible AI standards throughout the solution lifecycle.
QUALIFICATIONS
- 3–5 years of experience in software engineering, AI engineering, machine learning, data engineering, or a related technical discipline.
- Bachelor’s degree in Computer Science, Engineering, Data Science, Information Systems, or a related field (or equivalent practical experience).
- Experience building and deploying AI\-enabled applications or machine learning solutions in production environments.
- Strong programming skills in Python and experience with modern software engineering practices.
- Data pipelines, ETL/ELT processes, and API\-based integrations
- SQL and relational data modeling
- Cloud platforms such as Azure, AWS, or GCP
- Containerized and serverless application deployment patterns
- Building solutions on enterprise AI and data platforms such as Palantir Foundry, Azure AI, AWS, GCP, or Dataiku.
- Generative AI platforms and frameworks such as OpenAI, Anthropic, LangChain, LlamaIndex, Hugging Face, or similar technologies.
- Prompt engineering, evaluation methodologies, and retrieval\-augmented generation (RAG) patterns.
- Experience with Palantir Foundry is strongly preferred, including familiarity with:
- + Ontology\-driven application development
+ AIP and AI\-enabled workflows
+ Data products and pipeline development
+ Workshop applications and operational workflows
+ Code Repositories and software delivery within Foundry environments
+ Understanding of responsible AI concepts, including transparency, human oversight, security, and governance practices.
CHARACTERISTICS
- Strong problem\-solving and analytical skills with the ability to work through ambiguity and deliver practical solutions.
- Effective communication skills and the ability to collaborate with technical and non\-technical stakeholders.
- Curiosity and enthusiasm for emerging AI technologies and software engineering practices.
- Strong attention to quality, maintainability, and user experience.
- Ability to learn new technologies quickly and apply them effectively in a business environment.
- High degree of accountability, ownership, and commitment to delivering results.
*McCarthy is proud to be an equal opportunity employer, including disability and protected veteran status.*
*NOTICE TO EXTERNAL SEARCH FIRMS:* *McCarthy’s Talent Acquisition Team is the* *only authorized representative* *permitted to engage with external search firms, staffing agencies, or other third\-party recruiting partners. McCarthy maintains an Approved Agency List for recruiting partners, which is reviewed and updated annually.*
*McCarthy will only consider submissions from agencies with a signed fee agreement in place for the current year. McCarthy does not accept unsolicited resumes, candidate submissions, or referrals from agencies that do not meet these requirements.*
*If a candidate is submitted without an active agreement, McCarthy will have no obligation to pay any fees and reserves the right to contact, engage, interview, or hire such candidate(s) without any financial or other responsibility to the submitting agency. Unsolicited resumes, including those sent directly to hiring managers or other employees, will be considered the property of McCarthy.*
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 McCarthy Holdings, 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.
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.
McCarthy Holdings AI Hiring
McCarthy Holdings has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in St. Louis, MO, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 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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