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About This Role
Summary:
The Senior / Lead Data Software Engineer is responsible for the technical leadership, design, and delivery of the Bank's analytics and AI\-enabled software solutions. This role combines hands\-on engineering with architectural ownership and mentoring responsibilities. The position leads development of Power BI analytics solutions and AI\-assisted applications built on large language models, while establishing engineering standards, guiding other engineers, and ensuring solutions meet the Bank's security, data governance, and regulatory expectations — including strong protection of customer nonpublic personal information (NPI).
Essential Duties and Responsibilities:
Technical Leadership \& Architecture
- Serve as the technical lead for analytics and AI software solutions, owning design, implementation quality, and long\-term maintainability.
- Architect, develop, and optimize Power BI solutions, including semantic models, datasets, DAX, row\-level security, and performance tuning.
- Define and enforce standards for data modeling, Power BI development, version control, secure SDLC, and deployment practices — aligned to the Bank's ITIL\-based change management process.
AI/LLM Solution Development
- Design and implement AI\-powered applications and workflows leveraging large language models.
- Lead AI agent buildout, including agent architecture, orchestration logic, prompt engineering, evaluation, and operational guardrails.
AI Risk, Governance \& Data Protection
- Implement controls that prevent exposure of customer nonpublic personal information (NPI) to external AI services, including data minimization, masking/redaction in prompts, and prompt/response logging and retention.
- Ensure AI solutions are secure, auditable, and explainable, with defined human review before AI output enters a system of record or customer\-facing use; establish AI/model risk practices appropriate to the Bank, including documentation, validation, and ongoing monitoring for accuracy, drift, and unintended outcomes.
- Ensure AI and analytics access to governed data honors least\-privilege and data\-masking controls, without bypassing row\- or column\-level protections.
Cross\-Functional Partnership \& Delivery
- Partner with data platform, infrastructure, information security, and compliance stakeholders to align analytics and AI solutions with the Bank's architecture and risk expectations.
- Translate complex business problems into scalable software and AI solutions with measurable outcomes.
Mentorship, Documentation \& Innovation
- Mentor and coach other engineers through code reviews, design guidance, and ongoing technical development, ensuring adherence to architectural standards and best practices.
- Lead troubleshooting and root\-cause analysis for complex production issues across analytics and AI systems.
- Maintain comprehensive technical documentation, including architecture diagrams, standards, and operational runbooks.
- Evaluate emerging analytics and AI technologies and provide recommendations on adoption and roadmap alignment.
- Other duties as assigned.
Education And/Or Experience:
Bachelor's degree in Computer Science, Software Engineering, Information Technology, or a related field (or equivalent experience).
Minimum of seven (7\) years of experience in data engineering, analytics engineering, or software development roles.
Demonstrated experience leading technical design and delivery of production Power BI solutions.
Hands\-on experience designing and implementing AI\-driven applications using large language models (experience with Anthropic Claude strongly preferred).
Prior experience serving as a technical lead or senior engineer with mentoring responsibilities.
Knowledge/Skills/Abilities:
- Expert\-level proficiency with Power BI, including semantic modeling, advanced DAX, row\-level security, and performance optimization.
- Strong SQL skills and experience working with enterprise data warehouse platforms (e.g., Snowflake).
- Strong software engineering background with one or more programming languages (e.g., Python, C\#, or similar).
- Deep understanding of LLM\-based systems, including prompt engineering, agent design patterns, evaluation, and safety controls.
- Experience integrating AI agents with internal systems, APIs, and governed data sources under access and data\-protection controls.
- Familiarity with protecting customer NPI in analytics and AI workflows (data classification, masking/redaction, and least\-privilege access).
- Ability to establish and enforce engineering standards and guide architectural decision\-making.
- Strong leadership, mentoring, and communication skills with the ability to influence without direct authority.
- Highly analytical, detail\-oriented, and able to work under pressure of deadlines in a regulated environment.
- All candidates will be required to complete a pre\-employment background check, credit check, and drug screening.
Preferred Skills:
- Experience integrating AI capabilities into analytics or business intelligence platforms.
- Familiarity with cloud\-based data platforms (e.g., Azure/Snowflake) and CI/CD practices.
- Experience in financial services or other regulated industries.
- Knowledge of data governance, security, and AI/model risk practices as applied to analytics and AI.
Physical Requirements:
- Must exert up to 10 pounds of force occasionally and/or a negligible amount of force frequently.
- Sitting for long periods.
- Specific vision abilities required by this job include close vision and the ability to adjust focus.
Working Conditions:
The working conditions are generally comfortable, with minimal exposure to noise, heat, dust, and other related items. All employees are required to maintain a neat and safe work area.
Regent Bank is an Equal Opportunity Employer. We do not discriminate on the basis of race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, genetic information, veteran status, or any other characteristic protected by law.
Role Details
About This Role
AI Software Engineers build the applications and systems that AI models run inside. They own the API layers, data pipelines, frontend integrations, and infrastructure that turn a model into a product users interact with. Every AI company needs engineers who can build the software around the AI.
The challenge is building reliable systems around inherently unreliable components. Models are probabilistic. They'll give different answers to the same question. They hallucinate. They're slow. They're expensive. Your job is to build an application layer that handles all of this gracefully while delivering a product that users trust and enjoy.
Across the 4,317 AI roles we're tracking, AI Software Engineer positions make up 7% of the market. At REGENT BANK, this role fits into their broader AI and engineering organization.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
What the Work Looks Like
A typical week includes: building API endpoints that serve model inference with caching and fallback logic, designing the data pipeline that feeds context to a RAG system, implementing streaming responses in the frontend, debugging a race condition in the async inference pipeline, and optimizing database queries for the vector search layer. It's full-stack engineering with AI at the center.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
Skills Required
Full-stack engineering skills with AI integration experience. Python and TypeScript are the most common requirements. You'll need to understand API design, database architecture, and how to build reliable systems around probabilistic outputs. Experience with streaming, async processing, and caching patterns is increasingly important as real-time AI applications proliferate.
Knowledge of vector databases, embedding APIs, and LLM integration patterns (function calling, structured outputs, retry logic) differentiates AI software engineers from general software engineers. Understanding cost optimization (caching strategies, model routing, batched inference) is valuable since inference costs can dominate application economics.
Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
Compensation Benchmarks
AI Software Engineer roles pay a median of $218,500 based on 729 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
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.
REGENT BANK AI Hiring
REGENT BANK has 1 open AI role right now. They're hiring across AI Software Engineer. Based in Tulsa, OK, US.
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 Software Engineer roles include Software Engineer, Full-Stack Developer, Backend Engineer.
From here, career progression typically leads toward Staff Engineer, AI Architect, Engineering Manager.
If you're a software engineer, you're already 80% there. Learn the AI integration patterns: RAG, streaming inference, function calling, structured outputs. Build a project that demonstrates you can wrap an AI model in a production-quality application with proper error handling, caching, and user experience. That's the portfolio piece that gets you hired.
What to Expect in Interviews
Technical screens look like standard software engineering interviews with an AI twist. Expect system design questions about building reliable applications around probabilistic models: handling streaming responses, implementing retry logic for API failures, and designing caching strategies for LLM outputs. Coding rounds test standard algorithms plus practical integration patterns like async processing and rate limiting.
When evaluating opportunities: Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
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).
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
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.
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