Top AI & Machine Learning Jobs

Develop machine learning models supporting credit underwriting and business card strategies. Partner with data scientists, software engineers, product managers, and business stakeholders to design, train, validate, evaluate, backtest, and implement models. Analyze large numeric and textual datasets using Python, AWS, Spark, H2O, and related technologies, translating complex findings into business outcomes.

Lead the design, development, testing, deployment, and support of foundational AI systems, including model training, LLM inference, similarity search, guardrails, evaluation, governance, and observability. Optimize large-scale production AI systems for scalability, latency, throughput, cost, and hardware utilization. Collaborate with engineering, research, program management, and product teams while shaping the technical vision and roadmap for responsible AI platforms.

Develop and deploy applied generative AI and machine learning solutions using text, image, and audio data. Build, train, evaluate, validate, and operationalize NLP and deep learning models with LLMs, VLMs, and cloud technologies. Define AI observability and audit requirements, monitor non-deterministic system behavior, establish controls, curate training datasets, and collaborate with product, engineering, business, and data science teams to deliver scalable customer-facing solutions.

As a Director, Data Scientist - Generative AI Systems, you will lead a team to develop AI products using NLP and generative AI technologies, partnering with cross-functional teams. Your role involves leveraging open-source programming and machine learning to convert customer data into actionable insights and enhance customer interactions with financial services.

Leads data science for credit card acquisition decisioning, including systemic risk monitoring, statistical analysis, vulnerability detection, and multi-model infrastructure. Builds LLM and Agentic AI solutions, develops machine learning models, analyzes large-scale data, and partners with data scientists, analysts, and software engineers. Translates complex technical findings into business goals while guiding innovation and talent development.

Leads software engineering and MLOps initiatives, mentoring developers and managing Agile delivery. Builds scalable machine learning infrastructure, production data pipelines, model-serving platforms, and monitoring systems. Productionizes data science models as resilient microservices using Python and cloud-native technologies. Partners with product, data science, and technical stakeholders while driving enterprise ML platform transformation across high-stakes applications.

Lead the design, development, deployment, and operation of machine learning systems at scale. Responsibilities include building and validating models, creating optimized data pipelines, automating testing and deployment, monitoring and retraining production models, designing cloud-based ML architectures, and applying responsible AI, governance, security, and explainability practices. The role also involves technical leadership, cross-functional Agile collaboration, and mentoring or leading teams developing production ML solutions.

Manage a data science team developing scalable pipelines, analytical tools, machine learning credit models, and underwriting infrastructure. Lead analysis of large numerical and textual datasets, identify valuable data sources and APIs, optimize business programs, explore emerging technologies, and develop high-performing talent. The role requires extensive statistical modeling, machine learning, large-scale programming, databases, version control, and AWS experience.

Leads product strategy and a product management team for enterprise AI/ML and machine learning platforms. Defines Generative AI and ML platform vision, evolves capabilities for data scientists and software developers, drives platform adoption, and partners with adjacent platform leaders on an integrated ecosystem. The role requires deep technical expertise in AI/ML infrastructure, Generative and Agentic AI, cloud and open-source technologies, people leadership, and AI risk management.

Leads the design, development, deployment, and maintenance of production machine learning systems at scale. Responsibilities include developing and validating models, building optimized data pipelines, writing application code, automating testing and deployment, monitoring and retraining production models, designing cloud-based ML architectures, and applying responsible AI and governance practices. The role collaborates with Product, Data Science, and Agile engineering teams and may include people leadership.