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About This Role
Company Description
Vericast is the financial institution (FI) performance partner. We help banks and credit unions drive growth, improve efficiency, increase engagement and navigate change through the power of data, technology and people. Our advanced analytics, data\-driven insights and integrated solution set enable better execution with agility, precision and scale. That’s why thousands of financial institutions look to Vericast and our 150 years of financial services expertise to help them achieve more.
Job Description
The AI Engineer III is a senior hands\-on technical role responsible for the hands\-on development, delivery and support of Agentic AI applications across the enterprise. This individual will serve as the technical lead for a team of AI Engineers, setting engineering standards, collaborating in architecting agentic solutions, and ensuring AI systems are built reliably, securely, and in alignment with business objectives. The role partners closely with the VP Engineering and Data Strategy on execution strategy while owning day\-to\-day technical delivery and support.
Key Duties/Responsibilities
- Serve as hands\-on technical lead for the design, development, deployment and support of Agentic AI applications, including multi\-agent workflows and orchestration (25%)
- Hands\-on technical coloration, code review, support and mentorship to AI Engineers and contractors on the team (15%)
- Architect and implement RAG pipelines, vector search, embedding\-based retrieval, and LLM integration patterns for production use cases (15%)
- Design and build MCP Server integrations and tool\-use frameworks to extend agentic application capabilities (10%)
- Collaborate with the VP Engineering and Data Strategy and cross\-functional stakeholders to translate AI strategy and roadmap into technical execution plans (10%)
- Own selection and integration of AI frameworks and platforms, ensuring scalability, reliability, and security of deployed solutions (10%)
- Establish and enforce engineering best practices, including CI/CD, testing, and responsible AI guardrails for agentic systems (10%)
- Monitor emerging AI engineering trends, tools, and techniques and assess applicability to current initiatives (5%)
Qualifications
Education
- Bachelor's Degree in a technical or numerical field (Required). Master's degree preferred.
Experience
- 8\+ years' experience designing and developing software applications and/or data engineering pipelines
- 3\+ years' experience leading or providing technical directions to engineers on application or AI/ML delivery teams
- Experience in LangGraph, LangChain, RAG chatbots, vector databases, NLP\-based automation, MCP Server development, embedding\-based search, and LLM integration is required
- Experience in microservices architecture, Kubernetes, OpenShift, AWS and/or Azure, Spring Boot, Node.js, CI/CD, and DevOps automation is required
- Experience with Trino, Superset, Starburst SQL Server, ETL tools, S3, Airflow, Iceberg, PySpark, or Hive preferred
- Experience building financial services solutions in big data infrastructure preferred
Knowledge/Skills/Abilities
- Strong hands\-on expertise in machine learning frameworks, agentic AI architecture, and cloud computing platforms
- Proven ability to lead technical delivery and mentor engineers in a fast\-paced environment
- Strong communication and facilitation skills; able to translate business requirements into technical designs
- Working understanding of AI ethics, data privacy, and regulatory considerations relevant to AI systems
Industry Certifications (Preferred)
- AWS Certified Machine Learning Engineer \- Associate (MLA\-C01\) or AWS Certified AI Practitioner
- Microsoft Certified: Azure AI Engineer Associate (AI\-102\)
- Google Cloud Professional Machine Learning Engineer
- NVIDIA AI/Deep Learning certification (e.g., NVIDIA Certified Associate: AI Infrastructure)
- Databricks Machine Learning Associate
Additional Information Salary range: $120,000 \- $140,000 Annually
Applications will be accepted through August 25, 2026, after which the posting will be closed and no longer available for submissions.\*
The ultimate compensation offered for the position will depend upon several factors such as skill level, cost of living, experience, and responsibilities.
All team members are responsible for demonstrating the company's Core Values at all times and for using Performance Excellence principles to continuously improve effectiveness, efficiency, products, and services. This includes, but is not limited to, participating on improvement teams, recommending and implementing improvement ideas, and participating in training and other activities to keep up to date on processes, information, etc.
All team members are responsible for supporting and complying with internal and external audits, to include providing information, performing assigned tasks to ensure compliance, and preparing and maintaining evidence that key duties identified as internal controls have been performed.
All team members are responsible for supporting and complying with safety and security policies to promote a healthy working environment.
Vericast offers a generous total rewards benefits package that includes medical, dental and vision coverage, 401K matching andflexible PTO. A wide variety of additional benefits like life insurance, employee assistance and pet insurance are also available, not to mention smart and friendly coworkers!
At Vericast, we don’t just accept differences \- we celebrate them, we support them, and we thrive on them for the benefit of our employees, our clients, and our community. As an Equal Opportunity employer, Vericast considers applicants for all positions without regard to race, color, creed, religion, national origin or ancestry, sex, sexual orientation, gender identity, age, disability, genetic information, veteran status, or any other classifications protected by law. Applicants who have disabilities may request that accommodations be made in order to complete the selection process by contacting our Talent Acquisition team at [email protected] To review your rights under Equal Employment Opportunity please visit: www.dol.gov/ofccp/regs/compliance/posters/pdf/eeopost.pdf
Salary Context
This $120K-$140K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Vericast, 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($130K) sits 40% below the category median. Disclosed range: $120K to $140K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Vericast AI Hiring
Vericast has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in San Antonio, TX, US. Compensation range: $140K - $140K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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