Top AI & Machine Learning Jobs

Leads enterprise AI engineering strategy and multi-team delivery of scalable, responsible AI systems. Oversees foundation model training, LLM inference, similarity search, guardrails, evaluation, governance, observability, and production optimization. Establishes responsible AI standards, makes technology decisions, develops long-term platform roadmaps, partners with research and risk teams, and attracts and mentors engineering talent.

Design, develop, deploy, and support large-scale AI systems, including foundation model training, LLM inference, agents, vector search, guardrails, evaluation, governance, and observability. Optimize models and infrastructure for cost, latency, throughput, scalability, and hardware utilization. Architect multi-model orchestration pipelines, establish AI cost-performance governance, lead technical design reviews, shape foundational AI roadmaps, and mentor senior AI engineering leaders.

Leads data science and machine learning initiatives for payment-network applications, including fraud, risk, merchant acceptance, intelligence, and monitoring. Partners with data scientists, engineers, and product managers to deliver customer-focused products. Builds models through design, training, evaluation, validation, and implementation while translating technical findings into business outcomes and developing team talent.

Build and deploy scalable AI systems, including foundation model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize model training and inference for cost, latency, throughput, and hardware utilization. Own complex AI architecture, reliability SLOs, and technical roadmaps while collaborating across engineering, research, infrastructure, and product teams. Lead deployment reviews for security and compliance and mentor senior technical staff.

Leads data science for consumer and developer experiences, partnering with engineers and product managers to deliver customer-focused products. Builds, evaluates, validates, and deploys machine learning models using large-scale numerical and textual data. Applies statistical modeling, A/B testing, clustering, classification, sentiment analysis, time series, and deep learning while translating technical insights into business outcomes. The role also includes team leadership, talent development, and evaluating emerging AI and cloud technologies.

Build, deploy, scale, and monitor production machine learning models and infrastructure. Responsibilities include developing ML applications, optimizing algorithms and data pipelines, operating distributed systems and cloud services, automating testing and deployment, maintaining models, and applying responsible AI, governance, security, and explainability practices. The role collaborates with Product, Data Science, and Agile engineering teams.

Build, deploy, scale, and maintain production machine learning models and infrastructure. Responsibilities include model development, feature selection, tuning, validation, data pipeline construction, cloud deployment, Kubernetes operations, CI/CD, monitoring, responsible AI, and production troubleshooting. The role collaborates with Product, Data Science, and Agile engineering teams to deliver resilient large-scale AI solutions.

Leads an AI engineering team building scalable, responsible generative AI platform services, including foundation model training, LLM inference, similarity search, guardrails, evaluation, observability, and governance. Establishes technical strategy, optimizes production AI systems for cost and performance, manages cloud deployments, translates enterprise goals into roadmaps, and develops engineering talent.

Conduct applied AI research focused on multimodal guardrails and vision-language models. Build and evaluate foundation models across training, validation, optimization, and production implementation. Collaborate with data scientists, engineers, and product managers to deliver scalable AI products. Develop research libraries and platform code, investigate emerging methods, document findings, and contribute to technical reports or conference publications.

Leads an enterprise data science team developing agentic AI products and machine learning solutions. Responsibilities include defining modeling strategy, designing experiments, analyzing telemetry, influencing senior leaders, validating and stress-testing models, and overseeing the full model lifecycle from design through implementation. The role partners with engineering and research teams and requires extensive experience in large-scale analytics, machine learning, relational databases, cloud computing, and open-source programming.