Top AI & Machine Learning Jobs

Design and build AI-powered, cloud-native backend services and automation platforms to support security outcomes. Integrate data across systems, lead architecture for distributed, resilient systems, prototype emerging AI solutions, and mentor engineers while driving production deployments and operational excellence.

Build and operate scalable Golang backend services and APIs for a multimodal ML platform handling video, image, and document processing. Integrate ML/LLM inference, agentic workflows, and vector retrieval (Milvus). Deploy and manage services on Kubernetes with Terraform on AWS, ensure observability, reliability, and security, and collaborate with ML engineers to productionize pipelines and run on-call/incident response.

Lead execution of Comcast Advertising's enterprise AI strategy by translating priorities into plans, coordinating cross-functional initiatives, and driving AI adoption. Manage AI portfolio planning, track KPIs and ROI, support governance and enablement programs, facilitate stakeholder alignment, and ensure delivery across workstreams. Build scalable processes, maintain dashboards and documentation, and support change management, training, and communications to operationalize AI capabilities across the organization.

Lead design and delivery of AI-enabled product capabilities, including agentic workflows, LLM/agent productionization, distributed inference, model lifecycle, and reusable patterns. Drive cross-functional implementation, mentor engineers, improve performance/scalability, and ensure reliable, maintainable customer-facing AI features.

Lead AI engineering strategy and delivery for AI-enabled capabilities, agentic workflows, and intelligent automation. Manage and grow engineering teams, align cross-functional stakeholders, define roadmaps, launch LLM/ML products responsibly to production, and measure business impact while ensuring quality, security, and adoption.

The role involves designing and implementing machine learning systems, mentoring engineers, leading technical strategies, and ensuring system reliability and scalability.

Design, build, and operate production-grade agentic systems and workflows. Rewrite an existing application into an end-to-end agentic platform using a Google-centric stack (ADK, Gemini). Implement agent-to-agent, agent-to-UI, and human-AI orchestration, AgentOps/MLOps, monitoring, and evaluation (LLM-as-a-Judge). Advocate for ethical, reliable, scalable, and user-centric AI experiences while collaborating with product, UX, and platform teams.