Applied AI Engineer

Tompkins Community Bank•City of Batavia, NY

About The Position

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.

Requirements

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.
Ability to travel periodically to company locations, meetings, training sessions, or business-related events, as needed.

Nice To Haves

Experience working in enterprise, financial services, or other regulated environments preferred.
Experience with Azure, AWS, Google 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.

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.