Metropolitan Commercial Bank
Overview
Metropolitan Commercial Bank (the “Bank”) is seeking 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 the IT Application Development and Support team and collaborating with the Chief Artificial Intelligence Officer to transform innovative AI prototypes into robust production systems. The AI/ML Engineer will lead the deployment of high-impact AI capabilities 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 researchers, 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 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 such as 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 RAG, 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 seamless user experiences through 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. Salary
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 wide to reflect factors such as seniority, performance, experience, and applicable laws. Final offer amounts are determined by multiple factors including candidate experience and expertise. Equal Employment Opportunity
Metropolitan Commercial Bank provides equal employment opportunities to all employees and applicants 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 law. This applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation, and training. Apply for this job
To apply, submit your application through the Bank’s careers page. This will include providing your resume and basic information as requested. Voluntary Self-Identification
Voluntary self-identification is requested for government reporting purposes. Completion is voluntary and confidential; it will not affect your employment candidacy. See the Voluntary Self-Identification of Disability section for additional information. Form CC-305 and related details are provided as part of regulatory requirements when applicable.
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Metropolitan Commercial Bank (the “Bank”) is seeking 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 the IT Application Development and Support team and collaborating with the Chief Artificial Intelligence Officer to transform innovative AI prototypes into robust production systems. The AI/ML Engineer will lead the deployment of high-impact AI capabilities 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 researchers, 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 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 such as 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 RAG, 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 seamless user experiences through 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. Salary
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 wide to reflect factors such as seniority, performance, experience, and applicable laws. Final offer amounts are determined by multiple factors including candidate experience and expertise. Equal Employment Opportunity
Metropolitan Commercial Bank provides equal employment opportunities to all employees and applicants 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 law. This applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation, and training. Apply for this job
To apply, submit your application through the Bank’s careers page. This will include providing your resume and basic information as requested. Voluntary Self-Identification
Voluntary self-identification is requested for government reporting purposes. Completion is voluntary and confidential; it will not affect your employment candidacy. See the Voluntary Self-Identification of Disability section for additional information. Form CC-305 and related details are provided as part of regulatory requirements when applicable.
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