Global Vice President of AI Automation

About Us

We are a staffing services technology company that helps organizations design, build, and scale digital products and engineering capabilities. Our teams deliver end-to-end software development, engineering, and design services, and we provide flexible staffing solutions to augment internal teams with specialized talent—quickly and reliably.

About the Role

We are seeking an innovative and resilient Global Vice President of AI Automation to lead our distributed engineering team. Whether you’re a seasoned Machine Learning Architect ready to design complex neural networks or an NLP specialist optimizing large language models, you’ll have a pivotal role on our technology ladder. You will lead the full AI/ML development lifecycle, bridging data science, backend engineering, and product needs to build intelligent systems that solve real-world problems. You will set strategy, define roadmaps, mentor engineers, and drive high-impact AI initiatives across multiple client engagements and internal platforms.

Responsibilities

• Build & Solve
  • Design, develop, and deploy scalable AI/ML models and systems.
  • Diagnose model performance issues, optimize inference times, and guide integration of AI features into core products with clear, measurable outcomes.
• Leadership & Collaboration
  • Own the AI product roadmap in collaboration with Product Managers, Data Scientists, and Backend Engineers.
  • Lead cross-functional squads in an Agile environment to operationalize prototypes, share technical insights, and refine API documentation.
• Quality, Speed & Excellence
  • Ensure high model accuracy, low latency, and robust operationalization.
  • Manage multiple workstreams with a disciplined backlog, rigorous code quality standards, and reproducible experiments.
  • Establish and promote engineering best practices (CI/CD, testing, instrumentation, observability) across AI initiatives.
• People & Mentorship
  • Build, coach, and grow a high-performing AI/ML engineering team; foster a culture of learning, experimentation, and psychological safety.
  • Promote best practices for remote collaboration, knowledge sharing, and career development.
• Governance & Risk
  • Ensure compliance with data handling, privacy, and security requirements; address regulatory considerations relevant to client industries.
  • Drive responsible AI practices, including bias monitoring, interpretability, and auditability.

Requirements

• Experience
  • 10+ years of professional experience in Software Engineering, Data Science, or Machine Learning in production environments.
  • Prior leadership or management experience overseeing AI/ML programs or teams.
• Education
  • Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Mathematics, or a related field (or equivalent practical experience).
• Technical Aptitude
  • Proficiency with Python and modern ML frameworks (PyTorch, TensorFlow, Keras, scikit-learn).
  • Familiarity with MLOps tools (MLflow, Kubeflow, Weights & Biases) and cloud ML services.
  • Experience with deployment and monitoring of AI systems in production.
  • Excellent written and verbal communication; ability to translate complex mathematical concepts into actionable business insights for non-technical stakeholders.
• Problem Solving
  • Strong analytical mindset; ability to diagnose root causes of model failures and to drive lasting improvements.
• Engineering & Organizational Practices
  • Solid grounding in software engineering principles (CI/CD, Git, Docker, unit/integration testing); ability to deliver clean, maintainable, scalable code and systems.
• Remote Readiness
  • Proven capability to work effectively in a distributed, asynchronous environment; self-motivated, disciplined, and communicative.
• Adaptability
  • Calm under pressure when experiments fail; pivot strategies quickly and convert setbacks into learning opportunities.

Nice to Have

• Certifications: AWS Certified Machine Learning – Specialty, Google Professional Machine Learning Engineer, or DeepLearning.AI specializations.
• Specialized Skills: Experience with LLMs, Retrieval-Augmented Generation (RAG), vector databases (e.g., Pinecone, Milvus), or reinforcement learning.
• Industry Knowledge: Prior AI applications in FinTech, Healthcare, E-commerce, SaaS, or other relevant sectors.
• Thought Leadership: Technical blog writing, open-source contributions, conference presentations (e.g., NeurIPS, ICML, CVPR).
• Cloud & Infrastructure: Experience managing GPU clusters, serverless inference, and multi-cloud deployments (AWS, Azure, GCP).
• Compliance & Privacy: Familiarity with regulated environments (HIPAA, SOC 2), and applying differential privacy or federated learning techniques.

Pay

The base salary range for this role is $450,000 to $550,000, with exact placement determined by your skills, years of experience, and interview performance.

Benefits

• Equity: Competitive stock option package.
• Remote Setup: Home office stipend to get your workspace set up perfectly.
• Health: Comprehensive medical, dental, and vision insurance.
• Time Off: Flexible PTO policy + Company Holidays.
• Growth: Annual learning and development budget.
• Retirement: 401(k) matching plan.
• Performance incentives and professional development opportunities.