Top AI & Machine Learning Jobs

Develops data mining, analytics, reporting, AI tools, and scalable cloud applications for finance. Aggregates data, improves data infrastructure and processing workflows, ensures data integrity, and partners with finance and MIS teams on reporting and strategic insights. Leads MIS initiatives, supports model development and deployment, provides technical guidance, and recommends performance improvements based on financial trends.

Lead a data science team delivering analytics, machine learning, and AI solutions for wealth management. Partner with product, strategy, and operations stakeholders to frame problems, define success metrics, analyze client and portfolio outcomes, operationalize experimentation, and communicate executive-ready recommendations. Coach data scientists, develop scalable analytical capabilities, and identify high-value AI and agentic use cases that improve decision quality, client outcomes, and operational efficiency.

Develop and deploy AI and machine learning solutions for workforce analytics. Conduct statistical analyses, experiments, and model evaluations; build scalable model factories and evaluation frameworks; apply generative AI and agentic development approaches; collaborate with business, product, and technology teams; communicate insights; and ensure compliance with risk, control, and governance requirements.

Leads the design, development, and deployment of AI and machine learning solutions for workforce-focused products. Oversees research and development initiatives, guides teams from experimentation through production, develops model factories and evaluation frameworks, mentors data scientists, influences enterprise AI architecture, partners with senior stakeholders, and ensures responsible delivery within a regulated environment.

Develop and deploy production AI and machine learning solutions for Markets Operations, including process automation, anomaly detection, document intelligence, workflow optimization, and generative AI. Responsibilities include model development, MLOps, performance monitoring, dataset analysis, stakeholder collaboration, documentation, and continuous improvement of AI-driven business tools.

Develops Python-based data tools, datasets, and analyses supporting mortgage servicing rights valuation, back-testing, and risk management. Partners with quantitative modelers, business stakeholders, and technology teams to translate requirements into production-quality solutions. Applies machine learning, statistical modeling, generative AI, and data governance practices while managing projects end-to-end, documenting processes, and improving analytics capabilities.

Lead engineering and ML platform work: design, develop, and maintain secure, scalable production code and ML infrastructure; drive adoption and validation of enterprise AI-assisted development practices; automate remediation, evaluate vendors, and coach teams on responsible AI, resiliency, and security within agile CI/CD workflows.

Builds, scales, and maintains machine learning platforms supporting model training, deployment, monitoring, and lifecycle management. Develops secure platform services, APIs, automation, and CI/CD pipelines while integrating data engineering and feature management capabilities. Ensures reliability, scalability, and performance, produces architecture documentation, collaborates with data scientists and product teams, and promotes AI-assisted engineering practices and standardized validation across the team.

Designs, develops, and troubleshoots secure, scalable ML software systems and services. Builds production code, architecture artifacts, APIs, and distributed microservices; analyzes data to improve applications and system architecture. Uses AI-assisted development tools responsibly, validates generated outputs, and guides peers on secure engineering practices. Requires Python and ML framework expertise, cloud-native technologies, public cloud experience, distributed systems knowledge, and Cassandra or equivalent NoSQL experience. Preferred experience includes GPU workloads, model serving, distributed inference, model compression, and edge deployment.

Designs, develops, and troubleshoots secure, scalable ML systems and services. Builds production software in Python using ML frameworks, cloud-native technologies, distributed systems, microservices, and NoSQL databases. Creates architecture artifacts, analyzes large datasets, improves system reliability and performance, and uses AI-assisted development tools responsibly. Preferred experience includes GPU workloads in Kubernetes, model-serving frameworks, distributed inference, model compression, and edge deployment.