Interested in this AI/ML Engineer role at Tompkins Bank & Trust?
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About This Role
Overview:
The Applied AI Engineer is responsible for identifying, developing, and implementing practical artificial intelligence, machine learning, and automation solutions that drive business value, improve operational efficiency, and enhance decision\-making. This role partners across the organization to evaluate business opportunities, design and deploy production\-ready solutions, integrate third\-party AI technologies, and ensure AI systems are secure, scalable, compliant, and operationally sustainable. The position combines technical expertise, business acumen, and innovation to deliver measurable outcomes through AI\-enabled transformation.
Responsibilities:
- AI Strategy and Business Value Delivery – Identify, prioritize, and implement high\-impact AI and automation opportunities that drive measurable business outcomes, improve decision\-making, and support organizational objectives.
- AI Solution Development and Deployment – Design, build, test, and deploy scalable, production\-ready AI solutions, including intelligent assistants, machine learning applications, and workflow automation tools.
- Operational Efficiency and Process Optimization – Streamline business processes through automation and innovative technologies to increase productivity, reduce manual effort, and improve operational effectiveness.
- AI Governance, Reliability, and Risk Management – Ensure AI solutions meet security, compliance, governance, monitoring, reliability, and operational readiness standards while supporting responsible AI practices.
AI Opportunity Identification and Solution Delivery
- Identify, assess, prioritize, and deliver high\-value AI and automation initiatives aligned with business objectives.
- Partner with stakeholders to evaluate opportunities and determine appropriate AI\-driven solutions.
- Develop rapid prototypes and iterate solutions based on business feedback and operational needs.
AI Engineering and Automation Development
- Design, develop, implement, and support AI\-powered applications, intelligent assistants, and workflow automation solutions.
- Build end\-to\-end solutions that improve business performance and operational efficiency.
- Apply machine learning technologies, pre\-trained models, and cloud\-based AI services where appropriate.
Machine Learning Operations and Platform Management
- Establish and maintain MLOps practices to support model lifecycle management.
- Oversee model versioning, experiment tracking, testing, deployment, monitoring, drift detection, and retraining processes.
- Support reliable and scalable production AI environments.
Governance, Security, and Operational Excellence
- Ensure all AI solutions comply with governance, security, regulatory, reliability, and operational readiness requirements.
- Implement monitoring and support processes that promote system stability and long\-term performance.
- Apply responsible AI and data governance principles throughout solution development and deployment.
Vendor Evaluation and Technology Integration
- Evaluate, select, and implement third\-party AI, machine learning, and software\-as\-a\-service solutions.
- Determine the most effective approach for solving business problems, balancing build, buy, and integration decisions.
- Integrate vendor technologies into existing business processes, applications, and infrastructure.
Qualifications:
- Bachelor’s Degree in Computer Science, Data Science, Artificial Intelligence, Machine Learning, Software Engineering, Information Systems, or a related field required.
- Equivalent combination of education, professional certifications, and directly related experience may be considered.
- Minimum of five (5\) years of experience in software engineering, data science, applied artificial intelligence, machine learning, or a related technical discipline required.
- Demonstrated proficiency in Python or a comparable programming language with strong experience integrating APIs and developing end\-to\-end technology solutions.
- Experience developing, implementing, or supporting large language models (LLMs), workflow automation, and cloud\-based AI/ML services.
- Working knowledge of machine learning concepts, methodologies, and model deployment practices.
- Ability to evaluate business requirements and determine the most effective approach, including custom development, third\-party solutions, or integrated technology platforms.
- Understanding of production support concepts including monitoring, performance optimization, reliability, and operational readiness.
- Availability to participate in an on\-call support rotation as required.
- Experience working in enterprise, financial services, or other regulated environments preferred.
- Experience with Azure, AWS, Google Cloud Platform, or comparable cloud technologies preferred.
- Experience with machine learning platforms and tools such as Azure Machine Learning, MLflow, Databricks, SageMaker, Vertex AI, or similar technologies preferred.
- Experience building and managing machine learning pipelines including data preparation, training, evaluation, deployment, monitoring, and retraining preferred.
- Experience with AI development platforms and tools such as Microsoft Foundry, Azure OpenAI, Copilot Studio, or similar technologies preferred.
- Practical experience implementing MLOps practices and supporting production AI environments preferred.
- Knowledge of data governance, responsible AI principles, and AI risk management practices preferred.
- Ability to travel periodically to company locations, meetings, training sessions, or business\-related events, as needed.
Benefits:
- Medical
- Dental
- Vision
- 401(k) Match
- Profit Sharing
- Paid Time Off
- 11 Holidays
- Tuition Reimbursement
- Free Parking throughout Tompkins Community Bank
- Employee Referrals
EEO Statement:
Tompkins is committed to a policy of Equal Employment Opportunity ("EEO") with respect to all team members and applicants for employment and a work environment free from discrimination (including unlawful harassment) based on race, color, religion, sex, sexual orientation, transgender status, gender non\-conformity, gender identity, gender expression, national origin, age, marital status, domestic violence victim status, disability, predisposing genetic characteristics, military or veteran status or status in any group protected by federal, state, or local law.
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Pay Range: USD $105,000\.00 \- USD $145,000\.00 /Yr.
Salary Context
This $105K-$145K 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 Tompkins Bank & Trust, 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 ($125K) sits 42% below the category median. Disclosed range: $105K to $145K.
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.
Tompkins Bank & Trust AI Hiring
Tompkins Bank & Trust has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Batavia, NY, US. Compensation range: $145K - $145K.
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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