Gogo
Satellite Connectivity
Director,AI&MLEngineering
“Director, AI & ML Engineering at Gogo. Skills: AI Engineering, ML Engineering, MLOps, LLMOps. Lead engineering, delivery, operationalization of AI/ML capabilities. Build and mentor AI/ML engineering team”
What You'll Achieve.
Improve business efficiency; Enhance customer experience; Increase value of core products
Industry & Context.
Problem framing; Root cause analysis
What They're Looking For.
Must Have
Bachelor's Degree in Computer Science, Bachelor's Degree in Engineering, Bachelor's Degree in Data Science, 10+ years engineering experience, 6+ years delivering ML/AI solutions, 3+ years technical leadership role, Demonstrated success taking AI/ML systems into production, Experience with modern ML tooling and frameworks, Experience with cloud-based AI services, Proven ability to lead cross-functional execution, Proven ability to communicate effectively, Understanding of data privacy, Understanding of security, Understanding of governance practices
Nice to Have
Master's degree preferred, Working knowledge of DevOps, Working knowledge of distributed systems, Working knowledge of agile product delivery, Track record of measurable business impact from AI/ML initiatives
What You'll Do.
operationalization of AI/ML capabilities
Build and mentor AI/ML engineering team
Identify and deliver practical AI/ML solutions
Execute company’s AI strategy
Align AI strategy with business objectives
deployment of AI/ML solutions
Improve customer experience
Improve operational outcomes
Own end-to-end delivery lifecycle for AI/ML initiatives
Frame problems for AI/ML
Ensure data readiness for AI/ML
Conduct experimentation for AI/ML
Productionize AI/ML models
Continuously improve AI/ML models
Partner with Product to translate business goals
Develop actionable AI/ML roadmaps
Define measurable outcomes for AI/ML
Establish ML Ops practices
Establish LLM Ops practices
Define engineering standards for AI/ML systems
Ensure quality of AI/ML systems
Ensure reliability of AI/ML systems
Ensure security of AI/ML systems
Ensure latency of AI/ML systems
Ensure cost-to-serve of AI/ML systems
Ensure scalability of AI/ML systems
Collaborate with platform teams
Collaborate with data teams
Ensure foundations for data pipelines
Ensure foundations for feature management
Ensure foundations for model-ready datasets
Work with cross-functional stakeholders
Identify high-value AI/ML opportunities
Integrate AI/ML solutions into business workflows
Integrate AI/ML solutions into customer-facing experiences
Ensure AI/ML capabilities delivered as durable product features
Ensure AI/ML capabilities delivered as operational tools
Communicate plans to technical audiences
Communicate tradeoffs to technical audiences
Communicate progress to technical audiences
Communicate outcomes to technical audiences
Communicate plans to non-technical audiences
Communicate tradeoffs to non-technical audiences
Communicate progress to non-technical audiences
Communicate outcomes to non-technical audiences
Establish best practices for data governance
Establish best practices for privacy
Establish best practices for security
Establish best practices for responsible AI usage
Ensure compliance with internal policies
Ensure compliance with applicable regulations
Implement guardrails for model risk
Implement guardrails for bias
Implement guardrails for explainability
Implement guardrails for appropriate data use
Review processes for model risk
Review processes for bias
Review processes for explainability
Review processes for appropriate data use
Build AI/ML engineer team
Lead AI/ML engineer team
Develop AI/ML engineer team
Set expectations for team performance
Coach team performance
Foster culture of execution
Foster culture of continuous learning
Manage vendor relationships
Manage partnerships for AI/ML platforms
Manage partnerships for AI/ML tooling
Manage partnerships for AI/ML services
Support budgeting for AI/ML programs
Support capacity planning for AI/ML programs
Support budgeting for AI/ML platform investments
Support capacity planning for AI/ML platform investments
How You'll Work.
Team & Collaboration
Cross-functional stakeholders; Product teams; Operations teams; Support teams; Sales teams; IT teams; Security teams; Legal/Compliance teams; Platform teams; Data teams
Communication Scope
Technical communication; Non-technical communication
Process & Methodology
Agile product delivery
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