Architect, build, and deploy scalable ML systems: process massive datasets, design offline/online feature pipelines, train and productionize tree-based and deep models, implement low-latency serving and MLOps (retraining, CI/CD, monitoring), and run rigorous evaluation and A/B experimentation to drive measurable business impact.

Architect, build, and deploy low-latency, high-throughput ML systems across the full ML lifecycle: scalable feature pipelines, training/tuning of tree and deep models, production packaging/serving, MLOps automation, monitoring for drift, and rigorous A/B experimentation to drive business metrics.

Architect, build, and deploy production-grade ML systems: process massive datasets, design offline/online feature pipelines, train and productionize tree and neural models, implement MLOps, monitor drift, and run rigorous A/B experiments to drive business metrics.

The role involves building and deploying machine learning solutions, collaborating with stakeholders, conducting data analysis, and ensuring model performance in production.

Lead QA for ML web applications: develop and execute test plans and automated frameworks, design ML/LLM-specific tests, mentor junior QA engineers, define QA metrics, collaborate with ML and software teams, participate in releases, troubleshoot model issues, and ensure functional, regression, performance, and security testing.

Seeking a Senior Data Engineer to design and implement high-performance data solutions and pipelines, ensuring scalability and reliability in cloud environments.

Lead engineering across ML/MLOps and core software development: define strategy, scale ML platforms, integrate ML lifecycle with CI/CD and Kubernetes, ensure software engineering best practices, mentor distributed teams, and collaborate cross-functionally to deliver product vision.

The Senior Machine Learning Engineer will architect, build, and deploy high-performance machine learning systems, managing the entire ML lifecycle, including data processing and model deployment.

Lead enterprise revenue growth and manage sales teams; architect AI-driven solutions and collaborate with clients to solve business problems.

As an Applied AI Engineer, you will design, develop, and deploy AI automation solutions, integrating LLMs into business workflows, and collaborating with stakeholders. Responsibilities include building reliable systems, supporting clients, and enhancing features.

The Applied AI Engineer will design, develop, and deploy AI solutions, integrating LLMs into business workflows, and ensuring reliability and maintainability of systems.

The Applied AI Engineer will develop and deploy AI automation solutions, integrate LLMs, and collaborate with clients to enhance workflows and systems.

As an Applied AI Engineer, you will design, develop, and deploy AI and automation solutions, collaborating with various stakeholders. Responsibilities include building workflows, integrating LLMs, improving solutions, and producing documentation.

As an Applied AI Engineer, deliver AI and automation solutions from requirements to deployment, collaborating with stakeholders, designing workflows, and improving deployed systems.

As an Applied AI Engineer (Automation), you'll design, develop, and deploy AI and automation solutions, integrating LLMs into workflows and ensuring scalability and client satisfaction.

The Senior Cloud and DevOps Engineer will lead infrastructure automation, cloud deployments, and integration of IBM watsonx solutions, ensuring optimized configurations and CI/CD pipelines.

Lead the configuration, installation, and management of DevOps software, architect IBM Cloud environments, and optimize CI/CD pipelines to support AI initiatives.

Design and maintain infrastructure for advanced analytics and data integration. Implement Google Cloud's Unified Graph Fabric and build a Customer 360 Knowledge Graph.

Design, build, and maintain data infrastructure in Snowflake, focusing on data integration, processing, and analytics. Collaborate in Agile teams and implement advanced data solutions.

Design, build, and maintain data infrastructure using Snowflake for BI and analytics, focusing on security, performance, and problem-solving.