Mcbankny
Overview
Metropolitan Commercial Bank (the “Bank”) is a New York City-based, full-service commercial bank offering a broad range of banking products and services to individuals, small businesses, and corporate entities throughout New York State. The Bank operates banking centers and private client offices in Manhattan, Boro Park, Brooklyn and Great Neck on Long Island. It is a New York State chartered commercial bank, a member of the Federal Reserve System and the Federal Deposit Insurance Corporation, and an equal housing lender. The parent company is Metropolitan Bank Holding Corp. (NYSE: MCB). Position Summary:
Metropolitan Commercial Bank seeks a VP-level AI/ML Engineer to deploy AI solutions at enterprise scale with emphasis on Large Language Model (LLM) applications and modern MLOps/AIOps practices. This role sits at the intersection of data science and software engineering, reporting to the manager of IT Application Development and Support and collaborating with the Chief AI Officer to transform AI prototypes into robust production systems. The AI/ML Engineer will lead deployment of high-impact AI capabilities (e.g., generative AI, personalization engines, automation tools) and ensure scalable AI platforms that deliver real-world value. The role also includes designing, constructing, and maintaining the Bank’s AIOps solution, with Snowflake as the primary ML platform (e.g., Snowpark Python, UDFs/UDTFs, Tasks/Streams, and Snowflake-native ML). We have a flexible work schedule where employees can work from home one day a week. Essential duties and responsibilities: Establish and enforce architecture standards for production AI systems, including data pipelines, model serving infrastructure, and real-time inference services. Implement AIOps/MLOps pipelines for CI/CD of ML models, model governance, monitoring, and lifecycle management. Design and maintain scalable software applications with integrated AI/ML capabilities. Develop software architecture and design patterns to ensure performance and scalability. Implement and manage data pipelines for preprocessing and transforming data for AI/ML models. Integrate AI/ML models into production environments and optimize for reliability and scalability. Apply Site Reliability Engineering (SRE) principles and implement monitoring and alerting solutions. Conduct code reviews and provide technical guidance to junior developers. Stay current with advancements in software engineering and AI/ML technologies. Adhere to agile and lean software development best practices. Thoroughly document all developed models and processes according to relevant policies and standards. Support the production environment by resolving technical or functional issues in line with Bank procedures. Cross-Functional Collaboration: Partner with data scientists, AI scientists, product managers, data engineers, DevOps, and business stakeholders to operationalize AI algorithms. Mentor or train teams and coordinate between research-oriented AI scientists and engineering teams to continuously improve models with production feedback. Scaling & Performance: Ensure AI solutions perform at scale, handling thousands of daily inferences with low latency and high reliability. Optimize model serving using techniques like model compression, caching, and hardware acceleration. Implement robust monitoring and alerting for model performance to detect and address degradation (e.g., drift, latency issues). Required knowledge, skills and experience: LLM & GenAI Mastery: Expert in building and deploying LLM-based applications using retrieval-augmented generation, prompt engineering, and vector databases. Skilled in LLMOps tools (LangChain, LlamaIndex) and fine-tuning models for enterprise use, including agent-based architectures. MLOps & Cloud Infrastructure: Proficient in cloud ML platforms (AWS, GCP, Azure) and MLOps workflows. Uses Docker, Kubernetes, and IaC tools (Terraform, CloudFormation) for scalable deployments. Experienced in CI/CD, real-time inference, GPU optimization, and ML observability (Prometheus, Grafana, MLflow). Full-Stack Development: Capable of building end-to-end AI solutions, from front-end (React) to back-end APIs (Flask, FastAPI, Node.js). Skilled in integrating ML models with databases (SQL, NoSQL) and delivering robust software engineering. Proficient in Python (pandas, scikit-learn), deep learning (PyTorch/TensorFlow/Keras), NLP/LLMs, LangChain, embeddings/vector search, and classic ML. Experienced with Snowflake-native ML (Snowpark Python, UDFs/UDTFs, Tasks/Streams). Competent in data engineering (SQL, ETL/pipelines, Spark/PySpark) and handling large structured/unstructured datasets. Strong understanding of AI/ML algorithms, application architecture, and design patterns. Excellent problem-solving, analytical, communication, and collaboration skills. Preferred knowledge, skills and experience: Financial services domain experience (fraud risk, AML, underwriting, or commercial/treasury analytics). Hands-on experience with Snowflake ML/Snowpark (Python), Tasks/Streams, secure external functions, and feature management/registry tooling. Familiarity with fairness toolkits, XAI frameworks, and preparing models for validation, audit, or regulatory exams. Knowledge of SR 23-4 (third-party risk), NYC Local Law 144 (AEDT), NYDFS Part 500 (cyber). Ability to work in a constantly evolving environment. Excellent written and verbal communication skills. Strong listening and teaching abilities. Demonstrated analytical, troubleshooting, and problem-solving skills. Quick learner of new technologies. Self-directed with strong technology and communication skills. Ability to synthesize multiple sources of information and understand the broader operational context of the Bank. Collaborative team player with a practical and creative approach in a dynamic work environment. Ability to handle ambiguity, multitask, and adapt quickly to changing priorities. Potential Salary:
$130,000 - $200,000 annually This salary range reflects base wages and does not include benefits, bonus, or incentive pay. Salary bands are purposefully wide ranging to encompass the different factors considered in determining where a candidate falls in the range, including but not limited to, seniority, performance, experience, education, and any other legitimate, non-discriminatory factor permitted by law. Final offer amounts are determined by multiple factors including candidate experience and expertise and may vary from the amounts listed here. Metropolitan Commercial Bank provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state, or local laws.
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Metropolitan Commercial Bank (the “Bank”) is a New York City-based, full-service commercial bank offering a broad range of banking products and services to individuals, small businesses, and corporate entities throughout New York State. The Bank operates banking centers and private client offices in Manhattan, Boro Park, Brooklyn and Great Neck on Long Island. It is a New York State chartered commercial bank, a member of the Federal Reserve System and the Federal Deposit Insurance Corporation, and an equal housing lender. The parent company is Metropolitan Bank Holding Corp. (NYSE: MCB). Position Summary:
Metropolitan Commercial Bank seeks a VP-level AI/ML Engineer to deploy AI solutions at enterprise scale with emphasis on Large Language Model (LLM) applications and modern MLOps/AIOps practices. This role sits at the intersection of data science and software engineering, reporting to the manager of IT Application Development and Support and collaborating with the Chief AI Officer to transform AI prototypes into robust production systems. The AI/ML Engineer will lead deployment of high-impact AI capabilities (e.g., generative AI, personalization engines, automation tools) and ensure scalable AI platforms that deliver real-world value. The role also includes designing, constructing, and maintaining the Bank’s AIOps solution, with Snowflake as the primary ML platform (e.g., Snowpark Python, UDFs/UDTFs, Tasks/Streams, and Snowflake-native ML). We have a flexible work schedule where employees can work from home one day a week. Essential duties and responsibilities: Establish and enforce architecture standards for production AI systems, including data pipelines, model serving infrastructure, and real-time inference services. Implement AIOps/MLOps pipelines for CI/CD of ML models, model governance, monitoring, and lifecycle management. Design and maintain scalable software applications with integrated AI/ML capabilities. Develop software architecture and design patterns to ensure performance and scalability. Implement and manage data pipelines for preprocessing and transforming data for AI/ML models. Integrate AI/ML models into production environments and optimize for reliability and scalability. Apply Site Reliability Engineering (SRE) principles and implement monitoring and alerting solutions. Conduct code reviews and provide technical guidance to junior developers. Stay current with advancements in software engineering and AI/ML technologies. Adhere to agile and lean software development best practices. Thoroughly document all developed models and processes according to relevant policies and standards. Support the production environment by resolving technical or functional issues in line with Bank procedures. Cross-Functional Collaboration: Partner with data scientists, AI scientists, product managers, data engineers, DevOps, and business stakeholders to operationalize AI algorithms. Mentor or train teams and coordinate between research-oriented AI scientists and engineering teams to continuously improve models with production feedback. Scaling & Performance: Ensure AI solutions perform at scale, handling thousands of daily inferences with low latency and high reliability. Optimize model serving using techniques like model compression, caching, and hardware acceleration. Implement robust monitoring and alerting for model performance to detect and address degradation (e.g., drift, latency issues). Required knowledge, skills and experience: LLM & GenAI Mastery: Expert in building and deploying LLM-based applications using retrieval-augmented generation, prompt engineering, and vector databases. Skilled in LLMOps tools (LangChain, LlamaIndex) and fine-tuning models for enterprise use, including agent-based architectures. MLOps & Cloud Infrastructure: Proficient in cloud ML platforms (AWS, GCP, Azure) and MLOps workflows. Uses Docker, Kubernetes, and IaC tools (Terraform, CloudFormation) for scalable deployments. Experienced in CI/CD, real-time inference, GPU optimization, and ML observability (Prometheus, Grafana, MLflow). Full-Stack Development: Capable of building end-to-end AI solutions, from front-end (React) to back-end APIs (Flask, FastAPI, Node.js). Skilled in integrating ML models with databases (SQL, NoSQL) and delivering robust software engineering. Proficient in Python (pandas, scikit-learn), deep learning (PyTorch/TensorFlow/Keras), NLP/LLMs, LangChain, embeddings/vector search, and classic ML. Experienced with Snowflake-native ML (Snowpark Python, UDFs/UDTFs, Tasks/Streams). Competent in data engineering (SQL, ETL/pipelines, Spark/PySpark) and handling large structured/unstructured datasets. Strong understanding of AI/ML algorithms, application architecture, and design patterns. Excellent problem-solving, analytical, communication, and collaboration skills. Preferred knowledge, skills and experience: Financial services domain experience (fraud risk, AML, underwriting, or commercial/treasury analytics). Hands-on experience with Snowflake ML/Snowpark (Python), Tasks/Streams, secure external functions, and feature management/registry tooling. Familiarity with fairness toolkits, XAI frameworks, and preparing models for validation, audit, or regulatory exams. Knowledge of SR 23-4 (third-party risk), NYC Local Law 144 (AEDT), NYDFS Part 500 (cyber). Ability to work in a constantly evolving environment. Excellent written and verbal communication skills. Strong listening and teaching abilities. Demonstrated analytical, troubleshooting, and problem-solving skills. Quick learner of new technologies. Self-directed with strong technology and communication skills. Ability to synthesize multiple sources of information and understand the broader operational context of the Bank. Collaborative team player with a practical and creative approach in a dynamic work environment. Ability to handle ambiguity, multitask, and adapt quickly to changing priorities. Potential Salary:
$130,000 - $200,000 annually This salary range reflects base wages and does not include benefits, bonus, or incentive pay. Salary bands are purposefully wide ranging to encompass the different factors considered in determining where a candidate falls in the range, including but not limited to, seniority, performance, experience, education, and any other legitimate, non-discriminatory factor permitted by law. Final offer amounts are determined by multiple factors including candidate experience and expertise and may vary from the amounts listed here. Metropolitan Commercial Bank provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state, or local laws.
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