Top AI & Machine Learning Jobs

Lead the architecture and development of scalable, cloud-native AI products, intelligent agents, agentic workflows, and analytics platforms. Design production-grade systems using LLMs, retrieval, orchestration, memory, evaluation, and safety controls. Build APIs and distributed services with Python, Java, AWS, Azure, Databricks, and Spark. Drive technical execution, architecture reviews, DevSecOps, automation, reliability, governance, and mentoring across globally distributed engineering teams.

Lead the design and development of cloud-native, AI-powered products and agentic workflows. Architect reliable systems using LLMs, retrieval, orchestration, memory, evaluation, safety, and human-in-the-loop controls. Build scalable services, APIs, and analytics platforms with AWS, Azure, Databricks, Spark, Python, and Java. Drive architecture, technical execution, CI/CD, DevSecOps, testing, security, observability, modernization, and mentoring across globally distributed engineering teams.

Build and lead scalable full-stack, data-intensive applications using distributed systems, cloud technologies, Databricks, Spark, and AI agentic workflows. Responsibilities include system design, API and data architecture, performance optimization, troubleshooting, secure coding, testing, observability, CI/CD, production readiness, cross-functional collaboration, and mentoring engineers.

Leads hands-on development of AI-powered products, intelligent agents, and cloud-native analytics platforms. Designs agentic architectures with retrieval, orchestration, memory, evaluation, safety, and human-in-the-loop controls. Builds scalable services, APIs, and data applications using AWS, Azure, Databricks, Spark, Python, and Java. Drives architecture, technical execution, DevSecOps, testing, reliability, governance, and modernization while mentoring engineers and coordinating globally distributed teams.

Lead hands-on engineering for Mastercard’s experimentation and analytics platform, building scalable cloud-native services and AI-powered products. Design agentic systems using LLMs, retrieval, orchestration, memory, evaluation, safety, and human-in-the-loop controls. Guide AI solutions from proof of concept to production while ensuring reliability, security, observability, and cost efficiency. Develop distributed systems, APIs, and data-intensive applications using AWS, Azure, Databricks, Spark, Python, and Java. Lead architecture and code reviews, coordinate global teams, mentor engineers, and drive engineering excellence.

Lead design, build, and operate scalable data pipelines and products for foundation model training, inference, and evaluation. Own ingestion, transformation, batch/stream processing, feature engineering, data quality, lineage, governance, and performance tuning. Collaborate with AI, platform, and product teams to ensure production readiness, security, and observability. Provide technical leadership, code/design reviews, mentoring, and delivery planning across the program.

Lead design, build, and operate scalable data pipelines and products to support foundation models and AI workloads. Own ingestion, transformation, feature engineering enablement, data quality, governance, and production readiness while mentoring engineers and ensuring enterprise standards.

Lead design, build, and operate scalable data pipelines and products to support foundation model training, inference, and evaluation. Ensure data quality, governance, lineage, and performance across batch and streaming workloads; enable feature engineering and feature stores; collaborate with AI, platform, and product teams; mentor engineers and manage delivery risks.

Lead design, build, and operate scalable data pipelines and products for AI foundation models. Own ingestion, batch and streaming transforms, feature engineering and training datasets, ensure data quality, lineage, governance, performance tuning, and secure access. Collaborate with AI, platform, and product teams, lead technical reviews, mentor engineers, and contribute to program planning and delivery.

Lead the design, build, and operation of scalable data pipelines and data products to support foundation model training, inference, and evaluation. Ensure data quality, governance, lineage, and performant architectures, collaborate with AI engineers, and mentor data engineering teams.