Top AI & Machine Learning Jobs

Provide technical leadership for production AI and machine learning systems, including generative AI, intelligent agents, predictive modeling, and optimization. Design scalable AI platforms, APIs, services, data pipelines, and infrastructure; establish evaluation, testing, observability, security, and governance standards; optimize system performance, reliability, and cost; and partner with multidisciplinary teams to deploy and operate solutions. Mentor engineers and influence technical strategy across the organization.

Build scalable, full-stack, AI-enabled products for BlackRock’s Aladdin investment compliance and analytics workflows. Lead technical design, implementation, testing, deployment, production support, and continuous improvement. Apply generative AI, semantic search, retrieval, intelligent assistants, and agentic workflows responsibly. Partner with product, engineering, compliance, and business stakeholders to translate complex requirements into deliverable solutions. Influence architecture, champion DevOps and SRE practices, mentor engineers, and promote quality, resilience, security, observability, and maintainability across a global organization.

Leads a team of full-stack engineers while setting technical strategy, architecture, and roadmaps for enterprise portfolio design platforms. Drives delivery of scalable Python and Angular or React applications, engineering best practices, operational resilience, and Agile execution. Leads adoption of applied and generative AI, including intelligent workflows, automation, responsible AI governance, and AI-assisted development. Partners with business and technology stakeholders, mentors technical leaders, and delivers innovative solutions that improve advisor engagement, productivity, and client experiences.

Lead design and delivery of a scalable AI platform for Aladdin: build Kubernetes-based model deployment, cloud-native infrastructure, CI/CD for ML, ensure security/compliance, troubleshoot platform reliability, translate business needs into robust technical designs, and collaborate across global teams.

Designs and develops generative AI and machine learning solutions for fixed income investment and business problems. Builds and fine-tunes models, analyzes structured and unstructured data, engineers features, evaluates performance, and supports deployment through APIs and scalable pipelines. Collaborates with portfolio managers, researchers, data engineers, and stakeholders while applying responsible AI principles, data governance, and clear communication of insights.

Build and operate enterprise AI platform services, APIs, SDKs, agent runtimes, retrieval systems, evaluation frameworks, observability, guardrails, and governance controls. Lead architecture for scalable, secure, reliable cloud-native services; write production Python code; establish testing, CI/CD, and operational practices; and drive complex technical initiatives. Mentor engineers, shape platform roadmaps, and potentially assume broader people leadership as the team grows.

Leads flagship AI Labs initiatives from scoping and research through production deployment. Designs and evaluates AI agents and agentic workflows, guides production-grade architecture, establishes quality and safety measurement, and partners with business and engineering stakeholders. Manages and mentors 3–6 AI scientists while communicating technical outcomes to senior leaders and broader audiences.

Provide technical leadership for production AI and machine learning systems, including generative AI, intelligent agents, predictive modeling, and optimization. Design scalable AI platforms, APIs, infrastructure, data pipelines, and observability frameworks. Lead architecture through deployment and continuous improvement while optimizing performance, reliability, security, and cost. Partner with multidisciplinary teams, establish engineering standards, mentor engineers, and translate emerging AI capabilities into measurable business outcomes.

Provides technical leadership for production AI and machine learning systems, including generative AI, intelligent agents, predictive models, and optimization. Establishes architecture, engineering standards, evaluation and observability practices, and reusable AI platforms. Partners across business, engineering, data, risk, and control teams to deploy and operate scalable systems while optimizing performance, reliability, security, and cost. Mentors engineers and influences technical strategy.

Develop and productionize AI and machine learning solutions for client, investment, and operational problems. Responsibilities include designing AI agents and agentic workflows, prototyping with foundation models and agent frameworks, building evaluation and observability systems, ensuring reliability and compliance, translating business needs into technical solutions, leading major initiatives, and presenting research and technical work to senior stakeholders.