AI Engineer , NY

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, Dev Ops, 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 (Lang Chain, Llama Index) 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, Cloud Formation) 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, No
  
  SQL) and delivering robust software engineering.
• Proficient in Python (pandas, scikit-learn), deep learning (PyTorch/Tensor Flow/Keras), NLP/LLMs, Lang Chain, 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…