Top AI & Machine Learning Jobs

Leads the strategy, architecture, development, deployment, monitoring, and optimization of an agentic AI platform. Transforms products, processes, and workflows into scalable AI-enabled capabilities; establishes standards for performance, security, governance, and reliability; evaluates models and orchestration frameworks; partners cross-functionally on technical roadmaps; and builds and mentors high-performing engineering teams.

Serve as technical lead in pre-sales: architect and demo LanceDB solutions for RAG, vector search, and ML pipelines; run discovery sessions, build POCs, onboard partners, and relay customer feedback to product and engineering.

Build and deploy production generative AI and machine learning systems for utility clients. Own engagements end to end, including discovery, architecture, retrieval, model selection, evaluation, deployment, monitoring, operator-facing applications, and post-go-live support. Integrate with client data and business systems, establish AI safety and governance controls, manage scope and risk, and communicate with technical teams and executives. Contribute reusable accelerators, evaluation frameworks, and reference architectures across client engagements.

Owns enterprise AI platforms, including tenant administration, LLM gateways, MCP integrations, agentic workflows, cloud AI workloads, security, governance, cost monitoring, and adoption. Builds integrations and internal tooling primarily in Python and TypeScript across AWS and GCP. Partners with cybersecurity, AI governance, finance, and executives to enforce policies, operate reliable systems, and report on usage, performance, and spend.

Build and improve intelligent document-processing automations for complex utility bills. Analyze document variations, configure automation logic, troubleshoot issues, and collaborate with product engineering and operations teams. Develop subject-matter expertise in utility billing and data flows while applying document modeling, validation logic, AI tools, and related technologies to improve speed and accuracy.

Drives adoption and value realization of AI/ML models across Gap Inc. brands. Manages stakeholder relationships, creates model explainability materials, coordinates A/B tests, builds adoption dashboards, measures business impact, diagnoses adoption barriers, and integrates model recommendations into merchandising, planning, sourcing, and costing workflows. The role also delivers executive communications, trains business users, and partners with data science and engineering teams on workflow enablement and continuous improvement.

Owns statistical, Bayesian, and machine learning solutions from development through production. Builds models for lead scoring, retention, forecasting, and segmentation; applies LLMs to unstructured data; develops Databricks and Azure pipelines; creates React and FastAPI applications; and communicates analytical findings to business stakeholders across sports and entertainment functions.

Supports enterprise AI adoption by developing strategies, roadmaps, business cases, and implementation plans. Facilitates workshops, identifies and prioritizes AI use cases, delivers training and enablement programs, builds AI champion networks, researches emerging technologies, and tracks adoption outcomes. The role also applies change management, human-centered design, responsible AI practices, and generative AI expertise to translate capabilities into practical business recommendations.

Develops, deploys, monitors, and supports production machine learning and AI applications. Responsibilities include building Python services, APIs, data and feature pipelines, MLOps workflows, generative AI and agent-based solutions, and integrations with enterprise systems. The role applies software engineering, cloud, security, governance, observability, and responsible AI practices while collaborating with data science, engineering, architecture, security, DevOps, and business teams. This is an onsite position in Des Moines.

Design and build enterprise LLM-serving infrastructure on Kubernetes, including GPU scheduling, deployment, scaling, model lifecycle management, Helm-based packaging, offline installations, platform integrations, and production observability. Develop across a multi-service Go codebase, contribute design documentation and reviews, and help establish engineering direction for a remote-first team.