Interested in this AI/ML Engineer role at Valley Bank?
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About This Role
Responsibilities include but are not limited to:
- Analyze current\-state business processes and identify opportunities for automation and AI\-driven enhancement.
- Design, develop, and maintain automation solutions using RPA platforms (e.g., UiPath) and AI services.
- Build and support intelligent workflows that integrate AI Agents, decision logic, and orchestration across enterprise systems.
- Develop and maintain Intelligent Document Processing (IDP) solutions leveraging OCR, document classification, data extraction, and validation techniques.
- Collaborate with business and technology stakeholders to translate requirements into scalable automation and AI solutions.
- Create and maintain technical documentation, solution designs, process maps, and operating procedures.
- Perform testing, monitoring, and optimization of deployed automation and AI solutions to ensure reliability and performance.
- Gather and analyze process and model performance metrics to drive continuous improvement.
- Provide operational support and enhancements for existing automation and AI solutions.
Requirements:
Required Skills:
- Understanding of business process analysis.
- Understanding of the principles and common frameworks of successful business process analysis and change management.
- Understanding of Robotics Process Automation (RPA) and tools, such as UiPath.
- Understanding AI Tools and Industry Trends.
- Proven ability to understand business processes and translate business requirements into application functionality.
- Working knowledge of macro creation, as well as programming and SQL languages.
- Experienced usage of the MS Office toolset (Word, Excel, PowerPoint, Visio).
- Proven experience in providing service to internal stakeholders to achieve successful project outcomes.
- Demonstrated capability for problem solving, decision making, sound judgment, assertiveness.
Strong analytical and trouble shooting skills * \- general scripting skills a plus.
Preferred Skills:
- Experience with Agentic AI and AI workflow orchestration.
- Exposure to Generative AI (LLMs, prompt engineering, copilots, or AI assistants).
- Experience building or supporting Intelligent Document Processing (IDP) solutions.
- Knowledge of OCR, Computer Vision, and document classification/extraction techniques.
- Familiarity with Machine Learning concepts, model integration, or ML platforms.
- Experience integrating AI and automation solutions with enterprise platforms (e.g., CRM, ServiceNow, core banking systems).
Required Experience:
- High School Diploma or GED equivalent.
- Minimum of four (4\) years of process analytics.
- Familiarity with RPA technology and AI Technology.
Preferred Experience:
- Bachelor of Science in Computer Science, MIS, or related degree.
- Financial industry or banking background.
Additional Details : At Valley Bank, we believe in people's growth potential. We invest in it. We protect it. We focus it. For nearly 100 years, we've been the Bank that clients from every industry turn to for our expertise, strategies, and advice\-building the kind of trust that can fuel every goal. We are the leading relationship bank built for growth\-with over $60 billion in assets, 3,800 experts, and more than 200 consumer branches and commercial banking offices in communities across the US. At Valley, we're all driven by an ambition that goes deeper than just having a job. That's why when you work for us, we make it our goal to help you focus on what drives you\-working to turn your passions and strengths into assets you can use to propel your ambitions and build the professional legacy you want. Because when we say we're a relationship bank built for growth, that's not just reserved for our clients\-that includes all our associates as well. The AI Engineer is responsible for designing, building, and supporting intelligent automation and AI\-driven solutions that improve operational efficiency, accuracy, and customer experience. This role blends traditional RPA with modern AI capabilities including Agentic AI, Generative AI, Intelligent Document Processing, and Machine Learning to automate and augment end\-to\-end business processes.
Pay Transparency In order to support the Fair Compensation Strategy by the US Govt., HR Dept., clients are required to adhere to "Pay Transparency Law"; in the impacted states; that have mandated the employers to list the salary ranges in Job advertisements or postings for job opportunities and Job promotions.
Salary Context
This $98K-$171K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Valley Bank, 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($135K) sits 25% below the category median. Disclosed range: $98K to $171K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Valley Bank AI Hiring
Valley Bank has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Morristown, NJ, US. Compensation range: $171K - $171K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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