Senior AI Engineer

• Design and implement agentic AI workflows using UiPath and Salesforce Agentforce, combining LLMs, rules, APIs, and ML models for end-to-end automation.
• Develop reusable agent frameworks and components to standardize and accelerate AI solution delivery.
• Build, train, and deploy machine learning models (classification, regression, time-series, anomaly detection) using DataRobot, leveraging Snowflake and Databricks for large-scale data engineering.
• Integrate ML models into enterprise workflows, APIs, and downstream applications.
• Engineer integrations across enterprise systems using REST APIs, event-driven architectures, and robust data pipelines.
• Support CI/CD and MLOps practices for scalable model deployment, monitoring, retraining, and lifecycle management.
• Ensure compliance with AI governance and model risk policies, maintaining monitoring, documentation, and audit readiness.

• Hands-on experience with agentic AI, orchestration, or intelligent automation platforms (UiPath strongly preferred).
• Strong experience building and deploying ML models using DataRobot or comparable platforms.
• Solid experience working with Snowflake and/or Databricks for data and ML workloads.
• Proficiency in Python; experience with SQL and API-based integrations.
• Strong understanding of ML lifecycle, model evaluation, and production deployment.
• Exposure to LLMs, prompt engineering, RAG, and AI agents in enterprise environments.
• Experience operating AI solutions in regulated industries (financial services, healthcare, etc.).
• Familiarity with MLOps, feature stores, and model monitoring practices.

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:

• Bachelor's degree in Computer Science, Engineering, Data Science, or a related field.
• Minimum of 6 years of experience building production-grade AI, ML, or automation solutions and AI Technology.

Preferred Experience:

• Master's in Computer Science, MIS, or related degree.
• Financial industry or banking background.
• Experience with Salesforce Agentforce or Salesforce platform integrations.
• Experience contributing to AI architecture standards or internal platforms.

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 Senior AI Engineer will design, build, and operationalize agentic AI workflows and machine learning solutions across the enterprise. This role sits at the intersection of intelligent automation, generative AI, and enterprise data platforms, and will play a key role in scaling AI-driven workflows using UiPath, Salesforce Agentforce, DataRobot, Snowflake, and Databricks. You will partner closely with product, operations, risk, and technology teams to deliver AI systems that are production-grade, governed, and value-driven.