Responsibilities
- Define and execute the strategic vision for a next-generation agentic AI data platform, covering architecture, design, implementation, and ongoing evolution.
- Empower data science and engineering teams with self-service tools and infrastructure.
- Implement governance frameworks prioritizing data privacy and compliant, production-ready machine learning operations.
- Create the foundational infrastructure for consistent, scalable AI integration across all product lines.
- Recruit, manage, and lead a cross-functional team of data engineers and AI/ML specialists.
- Make informed technical decisions that balance performance, cost, accuracy, and reliability within budget constraints.
- Lead technical discussions on AI governance, including security, compliance, risk, and privacy policies.
- Convert high-level governance requirements into actionable engineering standards and enforceable technical safeguards.
- Develop reliable data pipelines and evaluation systems to support scalable dataset generation and repeatable benchmarking.
- Design and deploy a multi-tenant AI data platform on Azure with scalability and long-term maintenance in mind.
- Create a hybrid data architecture that supports transactional systems, agentic AI processing, and knowledge graph integration.
- Implement vector and graph database solutions to power retrieval-augmented generation and semantic search capabilities.
- Build a full MLOps ecosystem on Azure, including deployment automation, security controls, and system observability.
- Develop CI/CD pipelines using Azure tools to support continuous integration and model deployment.
- Establish real-time inference systems with monitoring, alerting, and automatic detection of model drift.
- Manage the full lifecycle of data systems, including integration with existing taxonomies and ontologies.
- Develop high-throughput graph query APIs for real-time access to complex supply chain data relationships.
- Implement automated data validation, conflict resolution, and quality checks to maintain graph integrity.
- Adopt Infrastructure as Code practices and build automated pipelines for data and model deployments.
- Enforce platform-wide standards for cost efficiency, model selection, runtime environments, and performance metrics.
- Collaborate with Security, Legal, and Privacy teams to translate AI policy into technical enforcement mechanisms.
- Build systems for generating synthetic data, running automated evaluations, and enforcing quality thresholds before release.
- Provide self-service tools equipped with detailed monitoring and system observability features.
- Enhance software delivery through AI-driven code analysis and review tools.
- Improve engineering productivity by integrating AI-assisted coding technologies into development workflows.
Benefits
- Performance-based bonus compensation
- Paid leave for vacation and personal time
- Comprehensive health coverage including medical, dental, and vision
- Retirement savings plan with 401(k) options
- Provision of reasonable accommodations for employees with disabilities
Work Arrangement
Remote (Worldwide)
Team
Will build and lead a platform team composed of data engineers and AI/ML engineers.
Other
- Occasional travel is required as part of the role.
- Employment is contingent upon successful completion of drug and background checks.
- Applicants who are US residents may choose to omit age-related information from submitted documents.
- The company does not accept unsolicited referrals from external recruitment agencies or headhunters.
