Top AI & Machine Learning Jobs

Develop, train, and deploy machine learning and generative AI models in production. Focus on engineering aspects: scalable software, MLOps/DevOps integration, CI/CD pipelines, monitoring (e.g., New Relic), and automation frameworks to ensure model reliability and performance.

Lead design and implementation of the enterprise AI platform, defining reference architectures, reusable patterns, and complex services (lifecycle registries, control plane, memory, model abstraction). Build agentic AI capabilities, ensure secure governed deployments, drive portability and engineering standards, review designs, mentor senior engineers, and deliver production-grade AI systems across cloud-native infrastructure.

Lead and build a small senior AI engineering team to design, build, and operate a governed enterprise AI platform and agentic AI solutions. Define architecture, roadmap, and governance; lead cloud and runtime architecture on Google Cloud; mentor engineers; partner with Security, Risk, and business stakeholders; implement AI FinOps, evaluation, and responsible AI controls; and deliver production-grade generative AI, retrieval, memory, orchestration, and agent lifecycle capabilities.

Design and operationalize GenAI-enabled analytics to drive personalization, automation, and decision intelligence. Build and maintain scalable data models and pipelines (DBT, Databricks, Python/SQL). Lead prompt engineering and LLM fine-tuning, conduct feature attribution and ROI modeling, run advanced experiments, and deliver executive-level visual analytics and strategic recommendations across enterprise domains.

Lead strategy and execution for enterprise intelligence by framing complex AI, data, and technology questions; partner with senior leaders to design and deploy intelligence products; create executive- and Board-level communications; run cross-functional initiatives and governance; and support enterprise planning, portfolio management, and adoption of AI-enabled capabilities.