Metropolitan Commercial Bank (the "Bank") is seeking a VP-level AI/ML Engineer to deploy AI solutions at enterprise scale, with a strong emphasis on Large Language Model (LLM) applications and modern MLOps & AIOps practices, transforming innovative AI prototypes into robust, scalable production systems.
Requirements
- 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.
- 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).
- 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.
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.
- Write clean, maintainable code in general-purpose programming languages (Python, Java, C, C++, Go).
- 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.
Other
- We have a flexible work schedule where employees can work from home one day a week.
- 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.
- Excellent problem-solving, analytical, communication, and collaboration skills.
- Financial services domain experience (fraud risk, AML, underwriting, or commercial/treasury analytics).
