Top AI & Machine Learning Jobs

Ten-week Advanced Analytics internship supporting data querying, data preparation, exploratory analysis, statistical modeling, machine learning, test design, and proof-of-concept development. The intern will apply AI and generative AI tools responsibly, evaluate analytical outputs and model performance, collaborate across business functions, complete analytics projects, and present findings to stakeholders.

Lead the design and delivery of enterprise AI platform capabilities and production agentic AI solutions. Build multi-agent systems, RAG and knowledge management solutions, cloud-native infrastructure, and MLOps/LLMOps capabilities. Partner with business, architecture, security, legal, and risk stakeholders; influence architecture decisions; mentor engineers; and establish reusable engineering, governance, monitoring, and operational practices.

Build enterprise AI platform capabilities and production AI applications, including agentic workflows, retrieval-augmented generation, semantic search, vector databases, cloud-native services, and AI model operations. Collaborate with senior engineers on orchestration, developer tooling, governance, security, responsible AI, monitoring, testing, and compliance. Use Python, Google Cloud Platform, Kubernetes, CI/CD, and infrastructure as code while developing scalable AI solutions.

Designs and delivers enterprise AI platform capabilities and production-ready agentic applications. Builds multi-agent systems, RAG and knowledge platforms, orchestration frameworks, cloud-native services, and developer tooling. Implements MLOps and LLMOps for deployment, evaluation, monitoring, observability, and optimization. Partners with stakeholders, contributes to architecture and engineering standards, and independently owns initiatives from design through production deployment.

Design, build, and operationalize ML and Generative AI solutions (including LLMs, RAG, and agentic AI) across the model lifecycle. Collaborate with data, engineering, and product teams to deploy scalable models on cloud platforms, implement MLOps/CI-CD, create APIs and lightweight UI prototypes, and ensure governance, monitoring, and responsible AI practices.

Leads end-to-end delivery of production AI, generative AI, and agentic AI solutions. Designs architectures using multi-agent orchestration, RAG, hybrid retrieval, and human-in-the-loop workflows; establishes evaluation, testing, observability, safety, and responsible AI practices. Partners with business and technical teams to measure value, mentors engineers and data scientists, defines engineering standards, and influences enterprise AI strategy and roadmaps.

Leads end-to-end AI, ML, generative AI, and agentic AI initiatives from opportunity assessment through production deployment and monitoring. Designs multi-agent systems, RAG and GraphRAG solutions, evaluation and safety frameworks, and AI-powered applications using the enterprise platform. Sets technical standards, mentors AI talent, partners with business and technology stakeholders, and measures delivered value. The role also drives responsible AI practices and AI-assisted engineering, with limited travel under 10%.

Lead end-to-end delivery of AI solutions (ML, Generative AI, agentic systems), design and deploy production-grade LLM/RAG and multi-agent architectures, define responsible-AI standards and evaluation, mentor engineers, and partner with stakeholders to measure and drive business value using cloud AI platforms and enterprise CI/CD/IaC.

Lead end-to-end delivery of production AI solutions (ML, GenAI, agentic systems, RAG) on the enterprise AI platform. Design, prototype, deploy, monitor, and evaluate solutions; set technical standards; mentor engineers; and partner with stakeholders to drive measurable business value and adoption.

Leads independent validation and governance of predictive, generative, and agentic AI models. Develops evaluation frameworks, benchmarks, testing protocols, monitoring, observability, and third-party validation practices. Translates regulatory and model-risk requirements into controls, standards, playbooks, and audit evidence. Presents findings to senior leaders and governance committees, mentors colleagues, and partners with data science, engineering, legal, compliance, cybersecurity, and risk teams.