Top AI & Machine Learning Jobs

Design, build, productionize, and operate supervised and unsupervised ML models, LLM solutions, risk features, and Databricks data pipelines for financial crime monitoring. Develop AI feedback and accuracy measurement, monitor model drift, bias, and performance, and support enterprise-grade ML applications in production. Collaborate with product, engineering, SRE, data science, compliance, and operations teams using agile processes.

Lead engineering for production-grade agentic AI and LLM platforms. Design AWS cloud-native services, RAG solutions, vector storage, agent memory, APIs, and microservices. Establish MLOps, monitoring, governance, security, observability, and responsible AI practices. Provide technical direction, make architecture decisions, review code, mentor engineers, and drive adoption of AI-assisted development standards across teams in a regulated enterprise environment.

Lead the research, architecture, and production delivery of agentic and generative AI solutions for complex banking operations. Design collaborating agents, reusable services, evaluation frameworks, and distributed machine learning systems. Establish experimentation, monitoring, safety, reliability, and governance practices while partnering with business, product, and engineering teams. Mentor engineers and researchers and drive technical alignment across stakeholders.

Conducts research on algorithms for large language models and agentic AI systems, improving learning efficiency, inference performance, accuracy, latency, and compute cost. Develops theoretical foundations, implements performant software, validates methods at scale, solves problems for internal teams, publishes findings, presents research, and contributes to intellectual property protection.

Build and troubleshoot secure, production-ready AI/ML and agentic software for JPMorganChase’s LLM Suite platform. Develop GenAI services using Azure OpenAI, AWS Bedrock, public cloud architecture, and modern agentic frameworks. Design microservices and APIs, integrate algorithms with existing systems, support operational stability, and collaborate with senior engineers on architecture and implementation. Explore agent-to-agent communication, Model Context Protocol, and agentic orchestration patterns.

Lead the design, development, and operation of an enterprise GenAI serving platform. Build high-throughput LLM inference, gateway, routing, GPU optimization, quantization, distributed serving, and observability capabilities. Establish reliability practices, SLOs, performance benchmarks, cost controls, and secure production standards. Drive adoption of agentic AI-enabled engineering workflows with governance, auditability, and human validation while influencing senior stakeholders and enabling reusable platform components across teams.

Build and deploy practical machine learning, deep learning, and generative AI solutions. Design and evaluate experiments, work with large language models and AI agents, write production-quality code, optimize model performance, and collaborate with product and engineering teams to deliver scalable AI systems.

Builds end-to-end machine learning and agentic AI systems, including data pipelines, model training, RAG, LLM guardrails, APIs, and full-stack interfaces. The role develops scalable services and PySpark/SQL workflows, operationalizes models with MLOps, and supports monitoring, evaluation, human-in-the-loop review, and responsible AI practices. It also uses enterprise-approved AI coding tools while validating generated outputs for quality, security, and correctness.

Design, prototype, and deploy production-ready machine learning and generative AI solutions and agentic workflows. Own end-to-end model delivery, data and feature engineering, evaluation, deployment, monitoring, retraining, and governance. Collaborate with product, engineering, and risk stakeholders and contribute reusable patterns and services to accelerate adoption and ensure responsible AI practices.

Develop marketing analytics and measurement frameworks for Chase Auto Marketing. Analyze complex datasets, define KPIs, optimize audience targeting, evaluate marketing effectiveness, create compelling visualizations, and communicate strategic insights to executives and cross-functional business and technical stakeholders. Collaborate with marketing analytics teams to standardize deliverables and improve marketing performance.