> Markdown version of [/jobs/ext/3949344-senior-vice-president-ai-machine-learning-software-engineer](https://www.wearedevelopers.com/jobs/ext/3949344-senior-vice-president-ai-machine-learning-software-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Vice President, AI / Machine Learning Software Engineer - **Company:** The Bank of New York Mellon Corporation - **Location:** New York, NY, United States - **Experience:** Expert - **Salary:** $200,000.0 - **Contract:** Permanent contract - **Skills:** Training Data, Artificial Intelligence, Cloud Engineering, Continuous Integration, Machine Learning, Software Safety, Software Engineering, Data Logging, Feature Store, Large Language Models, Llamaindex, Generative AI, Web Filtering, Build Management, Kubernetes, Low Latency, Data Analytics, Performance Monitor, Machine Learning Operations, Data Pipelines, Dynatrace - **Published:** October 27, 2026 - **Apply:** https://www.careerbuilder.com/job-details/senior-vice-president-ai-machine-learning-software-engineer-new-york-ny--1083078b-4b95-49c4-acb4-d81ac6bf4f77 ## About the Role Machine Learning, Software Engineering, Artificial Intelligence (AI), Financial Systems, Financial Services, Artificial Intelligence (AI), Global Financial Markets, Finance, Operational Audit, Training Data Sets, * Strong experience building and operating production ML or GenAI systems in enterprise environments. * Deep hands-on expertise with LLM orchestration frameworks, such as LangChain and/or LlamaIndex. * Experience with model registries and experiment tracking, such as MLflow or equivalent. * Solid understanding of Kubernetes-based deployments and cloud-native architectures. * Familiarity with feature stores, data pipelines, and retriever/index lifecycle management. * Proven experience implementing telemetry, logging, metrics, and distributed tracing for ML/AI workloads. * Strong knowledge of CI/CD practices for ML, GenAI, and data-driven systems. Preferred Qualifications * Experience operating LLM systems at scale, including multi-model or multi-provider strategies. * Exposure to AI safety, governance, and compliance frameworks in regulated environments. * Background in SRE, platform engineering, or MLOps, with a reliability-first mindset. * Ability to translate ambiguous GenAI use cases into robust, production-grade architectures. ## Description We are seeking a senior-level engineer to design, build, and operate production-grade GenAI and Retrieval-Augmented Generation (RAG) platforms at scale. This role focuses on industrializing LLM-based systems with strong guardrails, observability, evaluation frameworks, and operational rigor, ensuring reliability, safety, and cost efficiency across the full AI lifecycle. This role is located in Jersey City, NJ., * Design and build production-ready RAG pipelines, including retrieval, ranking, prompt orchestration, and response generation, with comprehensive guardrails, tracing, and observability. * Implement offline and online evaluation frameworks for prompts, models, and datasets, including quality, safety, latency, and cost metrics. * Own end-to-end lifecycle management for GenAI systems, covering prompt versions, model versions, datasets, and configurations. * Establish and maintain CI/CD pipelines for prompts, models, and data, enabling safe, repeatable, and auditable releases. * Implement cost and performance monitoring, including token usage, inference latency, throughput, and spend optimization. * Build and enforce safety mechanisms, such as content filtering, policy enforcement, red-teaming feedback loops, and abuse detection. * Define and operationalize incident management workflows, including alerting, triage, rollback mechanisms, and post-incident analysis. * Partner closely with product, platform, and governance teams to ensure GenAI solutions meet enterprise reliability, security, and compliance standards. * Mentor engineers and influence best practices for building scalable, trustworthy AI systems.