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Applied Ai Engineer Jobs in Rochester, NY (NOW HIRING)

Tompkins Community Bank

Applied AI Engineer

Batavia, NY · On-site

$105K/yr

Overview The Applied AI Engineer is responsible for identifying, developing, and implementing practical artificial intelligence, machine learning, and automation solutions that drive business value ...

MegazoneCloud

Sr. Solutions Architect AI

Rochester, NY · On-site

$170K - $195K/yr

Lead and grow a multidisciplinary team of engineers and data scientists, acting as a "force ... applied-AI innovation. What You Bring (Requirements) * Professional Experience: 8-12+ years of ...

Deloitte

Lead AI and Data Science Engineer II

Rochester, NY · On-site

$101K - $133K/yr

Lead AI and Data Science Engineer II Drive the design and delivery of advanced analytics ... Graduate degree in Applied Statistics, Computer Science, Life Sciences, Industrial-Organizational ...

Varsity Tutors

Applied Mathematics Tutor

Rochester, NY · Remote

$18 - $40/hr

Our AI-powered Tutor Copilot enhances your sessions with real-time instructional support, lesson ... engineering, physics, finance, and computational science applications. * Conceptual Teaching ...

Applied High Voltage

Site Manager

Elba, NY

Applied High Voltage (AHV) is an electrical engineering and construction company with expertise in ... use of AI note-takers, recording devices, or real-time transcription software is strictly ...

Applied High Voltage

Site Manager

Elba, NY · On-site

Applied High Voltage (AHV) is an electrical engineering and construction company with expertise in ... use of AI note-takers, recording devices, or real-time transcription software is strictly ...

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All JobsApplied Ai Engineer JobsApplied Ai Engineer Jobs in Rochester, NY

Applied Ai Engineer information

What are the key skills and qualifications needed to thrive as an applied AI engineer?

To thrive as an Applied AI Engineer, you need strong proficiency in programming (especially Python), machine learning algorithms, statistics, and a relevant degree in computer science or a related field. Familiarity with frameworks like TensorFlow or PyTorch, experience with cloud platforms (such as AWS or Azure), and knowledge of data management tools are typically required. Excellent problem-solving, communication, and teamwork skills help you translate complex models into real-world solutions and collaborate across disciplines. These competencies ensure you can effectively develop, deploy, and maintain AI systems that drive business value.

What does an applied AI engineer do?

An applied AI engineer develops and implements artificial intelligence models and algorithms to solve real-world problems. They work with data, machine learning frameworks, and programming languages like Python or TensorFlow to create practical AI solutions for businesses or products.

What is the difference between Applied Ai Engineer vs Data Scientist?

AspectApplied Ai EngineerData Scientist
Required CredentialsBachelor's or Master's in CS, AI, or related fields; experience with AI frameworksBachelor's or Master's in CS, Statistics, or related fields; strong analytical skills
Work EnvironmentDevelops and deploys AI models in production environmentsAnalyzes data to extract insights and build predictive models
Industry UsageUsed in tech, healthcare, finance for deploying AI solutionsUsed across industries for data analysis and modeling

Applied Ai Engineers focus on implementing and deploying AI models in real-world applications, while Data Scientists primarily analyze data to generate insights and build predictive models. Both roles require similar educational backgrounds but differ in their core responsibilities and work environments.

What are some common challenges applied AI engineers face when deploying AI models into production environments?

Applied AI Engineers often encounter challenges such as ensuring models perform consistently on real-world data, optimizing models for speed and scalability, and integrating AI solutions with existing systems. Managing data privacy, monitoring for model drift, and maintaining robust documentation are also key concerns. Collaboration with DevOps, data engineering, and product teams is essential to address these challenges effectively and deliver reliable AI-driven solutions.

How much does an applied AI engineer make?

An applied AI engineer's salary varies based on experience, location, and industry, but typically ranges from $80,000 to $150,000 annually. Senior roles or those with specialized skills in machine learning, deep learning, and programming languages like Python or TensorFlow tend to earn higher salaries.
What job categories do people searching Applied Ai Engineer jobs in Rochester, NY look for? The top searched job categories for Applied Ai Engineer jobs in Rochester, NY are:
What cities near Rochester, NY are hiring for Applied Ai Engineer jobs? Cities near Rochester, NY with the most Applied Ai Engineer job openings:
Applied Ai Engineer jobs near you
Infographic showing various Applied Ai Engineer job openings in Rochester, NY as of August 2026, with employment types broken down into 74% Full Time, 21% Part Time, and 5% Contract. Highlights an 72% Physical, 3% Hybrid, and 25% Remote job distribution.

Applied AI Engineer

Tompkins Community Bank

Batavia, NY • On-site

$105K/yr

Full-time

Medical, Dental, Vision, Retirement, PTO

Posted 9 days ago

Tompkins Community Bank rating

8.4

Company rating: 8.4 out of 10

Based on 12 frontline employees who took The Breakroom Quiz

40th of 170 rated banks

Job description

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.

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