Director of AI applied solutions

United Statesfull-timeexecutive

OtherDirector

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Quick Summary

Overview

About the Role Reporting to the CTO, our new ] Director of Applied AI Solutions is the senior, client-facing technologist who converts business opportunities into production-ready AI/ML and GenAI platforms on AWS, Azure, and Google Cloud.

Key Responsibilities

Lead discovery workshops to surface AI, ML, and GenAI use-cases. Develop business cases, TCO/ROI models, and executive roadmaps.

Requirements Summary

8+ years in data science, ML engineering, or AI architecture Demonstrated production delivery of applied AI (traditional ML, deep learning, GenAI/LLMs) on at least two hyperscalers.

Technical Tools

awsazuregcpsnowflakeci-cddeep-learningetlmachine-learning

About the Role

~1 min read

Reporting to the CTO, our new ] Director of Applied AI Solutions is the senior, client-facing technologist who converts business opportunities into production-ready AI/ML and GenAI platforms on AWS, Azure, and Google Cloud. Reporting to the CTO, you’ll consult with C-suite stakeholders, architect end-to-end solutions—spanning data ingestion to responsible-AI guardrails—and steer multidisciplinary engineering teams through build, deployment, and post-production optimization. You will deliver world-class outcomes to our customers on AWS, Azure, GCP, and with our leading AI, Data, and Security ISV partners.

Responsibilities

~1 min read

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Lead discovery workshops to surface AI, ML, and GenAI use-cases.
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Develop business cases, TCO/ROI models, and executive roadmaps.
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Design cloud-native reference architectures covering data pipelines, feature stores, model-training, vector databases, and real-time inference.
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Expert-level relationships with AWS, Azure, GCP, and key AI ISV partners.
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Represent the company at industry conferences, webinars, and roundtables.
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Publish white papers, deliver conference talks, and brief analysts to position the firm as an applied-AI innovator.
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Track emerging research (LLM fine-tuning, agent frameworks, privacy-preserving ML) and inject into offerings.

Requirements

~1 min read

8+ years in data science, ML engineering, or AI architecture
Demonstrated production delivery of applied AI (traditional ML, deep learning, GenAI/LLMs) on at least two hyperscalers.
2+ years progressive experience in cloud architecture, consulting, or solutions engineering; 5+ years leading cross-functional technical teams.
Hands-on expertise with MLOps stacks and IaC.
Experience integrating data platforms (Databricks, Snowflake) a plus.
Strong grasp of DevSecOps, IaC, container, CI/CD, SRE, and FinOps.
Demonstrated ability to present to C-suite audiences
Relevant certifications—e.g., AWS ML Specialty, Azure AI Engineer, Google Professional ML Engineer.
Bachelor’s in Computer Science, Data Science, or related field (Master’s/PhD preferred).