Senior Director, AI

Equinix

. Lead the design, engineering, and deployment of generative AI and machine learning systems across Sales, Marketing, and Customer Success .

Posted 10/21/2026full-timeUnited StatesSenior💰 $182,000 - $403,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in designing and deploying generative AI and machine learning systems, with a strong focus on integrating these solutions into CRM and customer workflows. Proven ability to lead teams, drive business outcomes, and establish best practices in AI delivery and engineering quality.

Highest-signal resume keywords

Generative AI ExpertiseMachine Learning Engineering LeadershipProficiency in PyTorch or TensorFlowExperience with AWS, GCP, or AzureStrong Understanding of Distributed Systems

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Generative AIMachine LearningPrompt EngineeringModel Fine-TuningRetrieval-Augmented GenerationCI/CD Best PracticesModel EvaluationReal-Time Inference SystemsBatch Inference SystemsData Science

Soft Skills

CollaborationLeadershipStrategic ThinkingProblem SolvingCommunication

Tools & Technologies

AWSGCPAzureSalesforceEnterprise CRM EcosystemsModern Data StacksAI PlatformsDeveloper Tooling

Certifications & Qualifications

Advanced Degree in Computer ScienceAdvanced Degree in Machine Learning

Industry Keywords

SaaSEnterprise SoftwareAI SystemsData SourcingModel DevelopmentWorkflow OrchestrationAccount ExpansionCustomer Retention

Tech Stack

Tools & technologies

AWSAzureDistributed SystemsGoogle Cloud PlatformPyTorchTensorflow

About the role

Key responsibilities & impact

Lead the design, engineering, and deployment of generative AI and machine learning systems across Sales, Marketing, and Customer Success
Design and deploy AI and ML solutions embedded in CRM and customer workflows
Build LLM-powered copilots, agentic workflows, retrieval-augmented generation pipelines, and predictive models
Own the lifecycle from data sourcing and model development through evaluation, deployment, and monitoring
Architect and guide model training and fine-tuning pipelines
Build real-time and batch inference systems, embedding pipelines, and vector database integrations
Establish best practices for CI/CD, experimentation, model evaluation, and observability
Design and deploy agent-based systems for multi-step reasoning, tool usage, and workflow orchestration
Establish reusable patterns for prompt management, tool integration, policy enforcement, and agent lifecycle management
Partner with Sales and go-to-market leadership to embed AI into deal strategy and customer engagements
Drive pipeline growth, deal velocity, account expansion, conversion improvement, productivity gains, and customer retention
Collaborate across Engineering, Data, Product, Sales, and Customer Success
Translate business problems into scalable technical architectures and align systems, data, and workflows
Lead and grow teams of ML engineers, ML scientists, and AI architects
Establish engineering quality, execution, technical depth, scalable development practices, and enterprise AI delivery models

Requirements

What you’ll need

12–15+ years of experience in AI/ML, data science, or distributed systems engineering
8+ years leading ML or AI engineering teams in platform or infrastructure environments
Proven track record delivering production-grade AI systems at enterprise scale
Experience partnering with Sales, Marketing, or Customer Success organizations to drive business outcomes
Deep hands-on experience with generative AI, including LLMs, prompt engineering, and finetuning
Strong experience with retrieval-augmented generation architectures and agentic AI systems
Proficiency with ML frameworks such as PyTorch or TensorFlow
Experience with AWS, GCP, or Azure and modern data stacks
Strong understanding of distributed systems, CI/CD, experimentation frameworks, and observability
Experience with Salesforce or enterprise CRM ecosystems
Experience building internal AI platforms or developer tooling
Background in SaaS or enterprise software environments
Advanced degree in Computer Science, Machine Learning, or a related field
15+ years experience
Bachelor's preferred

Benefits

Comp & perks

Employee Assistance Program
Health, life, disability, and voluntary insurance plans
Retirement plan contributions
Paid Time Off (PTO)
Paid holidays
Healthcare coverage complementing the provincial healthcare system (Canada)
Defined Contribution Pension Plan (DCPP)
Group Retirement Savings Plan (RRSP)
Tax-Free Savings Plan (TSFA)
Vacation and personal time (Canada)
Bonus eligibility
Potential equity offering depending on the position