Top AI & Machine Learning Jobs

Lead development of agentic AI platform capabilities, including orchestration frameworks, RAG systems, tool calling, memory management, APIs, and production deployment patterns. Drive architecture, platform roadmaps, reliability, monitoring, governance, privacy, model risk controls, and responsible AI practices. Partner with product, engineering, risk, and control teams while coaching engineers on secure AI-assisted development and communicating technical strategy to senior stakeholders.

Lead development of agentic AI platform capabilities, including orchestration frameworks, retrieval systems, APIs, SDKs, reference implementations, memory management, tool calling, and production deployment. Define platform roadmaps, architecture, reliability, monitoring, testing, governance, privacy, and model-risk standards. Partner with product, engineering, risk, and control stakeholders while guiding responsible AI-assisted software development and communicating technical strategy to senior leaders.

Lead the architecture, development, deployment, and operation of enterprise AI/ML solutions for wholesale payments. Build production applications involving NLP, document AI, OCR, computer vision, LLMs, RAG, and fine-tuning on AWS. Establish scalable data pipelines, MLOps, model governance, monitoring, and responsible AI practices while mentoring engineers, setting technical standards, and aligning multi-quarter roadmaps with senior stakeholders.

Own analytical integrity for a payments observability and AI platform. Responsibilities include analyzing transactional data, validating metrics and model behavior, back-testing anomaly detection and forecasting models, monitoring data and model drift, triaging analytical defects, creating executive-ready visualizations, and partnering with product and engineering teams on requirements and prioritization.

Designs, develops, experiments with, and productionizes machine learning, deep learning, and generative AI solutions. Builds LLM and agentic workflows, conducts model tuning and evaluation, optimizes system performance, and develops end-to-end Python solutions. Partners with product and engineering teams to integrate generative AI into scalable ML platforms and production services, with a focus on NLP, RAG, embeddings, inferencing, and MLOps.

Leads development of an enterprise Agent SDK and agentic AI platform. Designs production-grade agent capabilities, evaluation systems, model-serving workflows, retrieval-augmented generation, and tool-using agents. Writes code, optimizes performance, establishes engineering standards, and partners across product, data, platform, and application teams. Requires extensive experience building large-scale machine learning systems, cloud and Kubernetes workloads, and reliable generative AI solutions.

Leads development of resilient, scalable software and AI/ML platform services using Python and Java. Drives AI-assisted engineering practices, mentors teams, integrates enterprise systems, and builds fraud-detection and risk-assessment ML pipelines. Designs feature-store, model deployment, monitoring, and agentic AI components with production guardrails, governance, security, and auditability. Collaborates with architects, data scientists, business stakeholders, and infrastructure teams in an agile environment.

Develop and maintain Java and React applications using Spring Boot, microservices, AWS ECS, and DevOps practices. Build scalable cloud applications, data pipelines, integrations, and tooling; optimize performance and reliability; troubleshoot production issues; and collaborate with designers, product owners, engineers, and business stakeholders. The role also involves data-driven insights, Agile delivery, CI/CD, code reviews, and exploring AI-enabled user interface solutions.

Leads an AI Controls Enablement team developing, evaluating, and scaling automated controls across Asset and Wealth Management. Translates AI behavior and risks into actionable control findings, oversees tool submissions, conducts simulations and red-team exercises, establishes governance standards, leads the AI Controls Council, and reports progress and impact to senior leadership. Partners with Controls, Technology, and business stakeholders to deliver compliant, sustainable AI solutions.

Lead development of a production-grade AI platform for federated agentic workflows and sales assistants. Design, prototype, and deploy multi-step LLM agents with orchestration, routing, memory, retrieval, evaluation, and observability capabilities. Evaluate emerging AI techniques, improve quality through feedback loops and error analysis, establish safe deployment standards, and partner with product, engineering, quant research, vendors, and startups. The role requires hands-on Python development, AWS experience, and expertise in LLM agents, RAG, tool use, prompting, and evaluations.