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About This Role
Job Title
AI Developer
Banesco USA is seeking an AI Developer for our IT Department.
Primary Job Duties
*AI & Data Science Development*:
- Design and architect robust individual agents within Gemini Enterprise (Agent Space) to autonomously handle specific business tasks with high reliability.
- Orchestrate complex connected integrations using Gemini agents to fully replace and modernize entire legacy workflows, moving beyond simple task automation to full process reinvention.
- Design and deploy scalable AI solutions using Google Cloud Platform (GCP) technologies (e.g., Vertex AI, BigQuery ML, AutoML) to solve complex banking challenges.
- Develop advanced predictive models and forecasting tools to analyze historical data, identify trends, and provide actionable insights that increase data-driven decision-making across the organization.
*Workflow Optimization & Innovation*:
- Apply creative problem-solving to design novel automation workflows that address non-standard, "leading-edge" business problems.
- Integrate Generative AI and Machine Learning models into enterprise applications and Business Intelligence systems to enhance user experience and operational efficiency.
- Collaborate with business units to identify, analyze, and document business processes suitable for intelligent automation.
- Develop and optimize complex SQL queries and data pipelines within GCP to support high-performance model training and automated inference.
*Support, Collaboration & Culture*:
- Provide ongoing maintenance and support for deployed models and agents, utilizing MLOps best practices to monitor performance, accuracy, and data drift.
- Maintain constant communication within the automation team, actively cross-training and sharing knowledge of new AI developments and solutions.
- Support business drivers through the development of AI analysis tools.
- Champion the establishment of an enterprise data-driven culture by mentoring teams on the capabilities of AI and demonstrating the value of predictive analytics.
*Compliance & Risk Management*:
- Ensure all developed AI solutions comply with the bank’s policies and procedures, including IT policy, Risk Management, and regulatory guidelines (e.g., BSA/AML).
- Implement appropriate security measures and data governance standards within GCP and Gemini environments to protect sensitive client data.
- Abide by the bank’s Enterprise Risk Management Policy (ERM), embedding risk identification and mitigation into daily modeling activities.
*Professional Development*:
- Willingness to work occasionally outside of normal business hours as required to support critical deployments or incidents.
- Complete all assigned annual training programs to stay current with technology trends and banking regulations.
- Perform other functions and/or duties as assigned by the Automation Team Lead or VP, BI & Automation Manager.
Job Requirements
*Education*:
- Bachelor’s degree in Computer Science, Information Systems, Data Science, Mathematics, Statistics, or a related field, or equivalent practical experience.
*Experience*:
- Banking Experience: Minimum of three (3) years of experience in the banking or financial services industry is preferred, with specific exposure to core banking systems (e.g., Jack Henry & Associates) and compliance regulations.
- Minimum of five (5) years of hands-on experience in software development and Data Science, with a specific focus on predictive modeling and forecasting.
- Proven experience developing and deploying AI/ML solutions on Google Cloud Platform (GCP) (e.g., Vertex AI, Cloud Functions, BigQuery).
- Gemini Enterprise: Demonstrated experience building Agent Space solutions, creating individual agents, and linking them to create complex, autonomous workflows.
- Demonstrated experience in applying creative technical solutions to undefined or complex business problems.
*Technical Proficiencies*:
- Cloud & AI: Deep proficiency in Google GCP ecosystem (Vertex AI, TensorFlow, Gemini models) and Gemini Enterprise (Agent Space) architecture.
- Programming: Advanced scripting skills in Python (Pandas, NumPy, Scikit-learn) and R.
- Databases: Strong proficiency in SQL (client and server), BigQuery, and experience with Oracle or AS400.
- Banking Systems: Familiarity with Jack Henry & Associates or similar core banking platforms is a plus.
- General Software: Proficiency in Google Workspace Apps (Docs, Sheets, Slides, etc.) and Microsoft Office Suite (Word, Excel, PowerPoint).
*Skills & Competencies*:
- Creative Problem-Solving: Ability to think outside the box to design innovative AI solutions for complex, non-linear business workflows25.
- Data Science Acumen: Deep understanding of statistical analysis, regression modeling, and time-series forecasting.
- Language: Bilingual proficiency in both English and Spanish (fluency in speaking, understanding, reading, and writing) is highly preferred, given the bank's international and domestic client base in South Florida.
- Communication: Strong verbal and written communication skills with the ability to articulate technical concepts clearly to both technical and non-technical audiences across all levels of the organization.
- Problem-Solving: Strong analytical and problem-solving skills to troubleshoot technical and non-technical issues, identify root causes, and propose effective solutions.
- Attention to Detail: Meticulous attention to detail and accuracy in all aspects of work.
- Organization & Time Management: Ability to multi-task effectively, strong organizational skills, and excellent time-management and prioritizing abilities to meet deadlines in a fast-paced environment.
- Customer Service: Strong customer service orientation, ensuring satisfaction of both internal and external stakeholders.
- Adaptability: Ability to work independently and under pressure, making sound decisions according to established guidelines and accomplishing tasks accurately and on time.
- Documentation: Proven ability to create clear, concise, and professional technical documents and reports for various audiences, including management.
Benefits & Perks
- Competitive base salary.
- Paid time off.
- Hybrid schedule.
- 401k with employer match.
- Tuition reimbursement.
- Paid parental leave.
- Medical, Dental, Vision.
- Life Insurance.
- Supplemental Insurances.
- Short-Term & Long-Term Disability Benefits.
- Free parking.
- On-site Cafeteria.
About Us
Banesco USA is part of Banesco International, a worldwide group of financial institutions with presence in 6 countries.
As a corporation in continuous evolution, we promote the ongoing professional and personal development of our employees, by embracing challenges and adapting to the changing environment of today’s world. We aim to develop integral human beings, committed to making a difference at the workplace and out in the world.
Our actions are rooted in our Values: Reliability, Responsibility, Quality and Innovation. We believe that we all have the same ability to transform our daily tasks into significant contributions, and therefore, Leave Our Mark.
At Banesco USA, one of our most valued assets is our enthusiastic team, which strives every day to create a world-class organization in an ever-changing world. Together, our team has made us a market leader and we invite you to join us.
#LI-HYBRID
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 3,897 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Banesco USA, 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 $154,000 based on 8,743 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $147,000.
Across all AI roles, the market median is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $85,000; Mid: $147,000; Senior: $225,000; Director: $230,600; VP: $248,357.
Banesco USA AI Hiring
Banesco USA has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Miami, FL, US.
Location Context
Across all AI roles, 16% (615 positions) offer remote work, while 3,251 require on-site attendance. Top AI hiring metros: New York (1,633 roles, $204,100 median); Los Angeles (1,356 roles, $179,440 median); San Francisco (1,230 roles, $240,000 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 3,897 open positions tracked in our dataset. By seniority: 111 entry-level, 1,958 mid-level, 1,413 senior, and 415 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (615 positions). The remaining 3,251 roles require on-site or hybrid attendance.
The market median for AI roles is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. Highest-paying categories: AI Engineering Manager ($293,500 median, 21 roles); AI Safety ($274,200 median, 24 roles); Research Engineer ($260,000 median, 264 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 3,897 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,733), Data Scientist (273), AI Software Engineer (271). 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 (111) are outnumbered by mid-level (1,958) and senior (1,413) 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 415 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (615 positions), with 3,251 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 $190,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $300,688. 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 $293,500 median, while Prompt Engineer roles sit at $145,600. 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 (2,064 postings), Aws (1,085 postings), Azure (867 postings), Rag (865 postings), Gcp (697 postings), Pytorch (650 postings), Prompt Engineering (597 postings), Kubernetes (499 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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