Top AI & Machine Learning Jobs

Set technical direction for Root’s AI platform, including infrastructure, tooling, shared services, governance, reliability, security, observability, and cost management. Lead cross-team initiatives, guide AI and LLM architecture decisions, establish operating standards, shape technical roadmaps, resolve complex tradeoffs, and mentor engineers and technical leaders across the organization.

Build and deploy AI agents, LLM workflows, and marketing automation systems for CRM and lifecycle campaigns. Automate Braze campaign development, segmentation, personalization, and pre-send QA. Create evaluation suites for brand voice, factual accuracy, and tone; maintain version-controlled prompts and agent definitions; integrate systems through APIs and MCP servers; train marketing users; and measure adoption, efficiency gains, and marketing KPI impact.

Partners with enterprise sales teams to advise CIOs and technical executives on AI architecture, governance, security, integrations, and revenue workflows. Designs reference architectures, technical roadmaps, validation plans, and business cases for Gong’s AI platform, while addressing build-versus-buy decisions and enterprise risk. Leads executive workshops, technical reviews, proofs of concept, and complex pursuits; enables Solutions Engineers through reusable assets and thought leadership; and provides market feedback to Product and Engineering.

Designs, builds, and operationalizes secure AI-powered data experiences, including natural-language analytics agents and agentic workflows. Owns AI tooling, retrieval, prompting, evaluation, Azure runtime architecture, deployment, monitoring, and supportability. Partners with data engineering, cybersecurity, IT, and business stakeholders to ensure governed, explainable, privacy-preserving solutions. Develops reusable AI components, responsible-AI guardrails, enablement materials, and adoption strategies for enterprise users.

Owns Vuori’s AI platform strategy and builds production-ready AI applications, agentic systems, orchestration frameworks, RAG architectures, and intelligent workflows. The role establishes engineering standards for evaluation, monitoring, security, governance, and reliability; integrates LLMs with enterprise systems; guides engineers; partners with business leaders to identify high-value opportunities; and measures business impact.

Own the technical design and delivery of multi-component AI systems, including LLM-backed features, agent tool orchestration, evaluation frameworks, data pipelines, and model harnesses. Build reliable APIs, SDKs, and feedback loops; optimize latency and cost; assess external models and datasets; and support production systems through on-call rotation. Mentor engineers and collaborate across search, location intelligence, and platform teams.

Owns architecture, technical standards, and production delivery for Infrastructure AI Ops solutions, including telemetry pipelines, topology graphs, infrastructure-as-code remediation, anomaly detection, capacity modeling, and autonomous self-healing automation. Serves as a critical-incident escalation point, makes automation safety decisions, develops governance evidence for regulated environments, mentors senior engineers, and remains hands-on with production code and infrastructure.

Leads AI product engineering and enterprise deployment of Generative and Agentic AI solutions. Owns AI product roadmaps, solution architecture, experimentation, MVP development, and scaled production delivery. Partners with product managers, data scientists, architects, executives, and business leaders to deliver measurable value. Manages and develops technical engineering teams while championing AI-native development, automation, testing, CI/CD, observability, and cloud-native software practices.

Build foundational AI-enabled applications, prototypes, prompts, tests, documentation, workflow automations, and agentic solutions. Translate requirements into software components, scripts, integrations, and configurations while learning SDLC, security, privacy, and responsible AI practices. Support prompt engineering, retrieval-assisted use cases, API integrations, unit testing, troubleshooting, code reviews, demonstrations, and documentation. Collaborate with technical and non-technical teams and use approved AI-assisted development tools under senior guidance.

Leads the organization’s AI strategy, roadmap, and implementation across manufacturing and distribution operations. Identifies high-impact use cases, builds business cases, oversees pilots and system integration, improves data governance, establishes responsible AI policies, and drives adoption through training and change management. Measures operational and financial outcomes while partnering with executives, technical teams, and facility employees across multiple locations.