DataHub
AI Tech Lead, Bay Area Hybrid
DataHub - Palo Alto, California, United States, 94306
Work at DataHub
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Overview
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AI Tech Lead, Bay Area Hybrid
role at
DataHub .
Find out if this opportunity is a good fit by reading all of the information that follows below. DataHub, built by Acryl Data, is an AI & Data Context Platform adopted by over 3,000 enterprises, including Apple, CVS Health, Netflix, and Visa. It offers metadata graph solutions for enterprise AI and data asset management, with a SaaS offering called DataHub Cloud that enhances AI discovery, observability, and governance.
Role Overview We seek an experienced AI Technical Lead to drive our AI initiatives, focusing on metadata management and AI infrastructure strategy. This role involves technical leadership in AI features and strategic planning for enterprise AI deployment, ensuring governance and efficiency.
Key Responsibilities
Lead the development of AI-powered features such as data classification, PII detection, and sensitive data identification.
Architect scalable ML pipelines for continuous learning and updates.
Design systems for model monitoring, validation, and performance tracking.
Implement privacy-preserving ML techniques and ensure compliance.
Shape metadata frameworks supporting AI systems, including model cards and lineage tracking.
Define standards for AI metadata management, including versioning and deployment configurations.
Develop systems for AI asset management across the development lifecycle.
Establish best practices for AI observability and governance.
Lead architectural decisions for AI system integration.
Mentor team members on ML engineering and AI system design.
Collaborate with product management on AI feature roadmap.
Engage with customers to understand AI infrastructure needs.
Minimum Qualifications
8+ years of software engineering experience, with at least 4 years in ML/AI.
Experience with ML frameworks (PyTorch, TensorFlow) and MLOps tools.
Knowledge of LLM deployment, fine-tuning, and operational considerations.
Experience with AI governance, bias detection, and fairness metrics.
Strong background in data privacy and security in AI.
Experience with enterprise AI deployment and infrastructure management.
Proficiency in Python and AI development tools.
Understanding of vector databases, embedding systems, and semantic search.
Experience with distributed systems and scalable architecture.
Preferred Qualifications
Experience with DataHub is a plus.
Experience building AI features in SaaS products.
Background in data catalog or metadata systems.
Knowledge of AI governance frameworks.
Experience with AI infrastructure cost optimization.
Understanding of AI regulatory requirements.
Track record of production ML systems.
Essential Knowledge Areas
Enterprise AI infrastructure components: model serving, vector databases, training infrastructure, feature stores, model monitoring, governance tools.
Enterprise AI deployment considerations: cost, security, compliance, performance, resource management, versioning.
If you're passionate about technology and customer engagement, and want to be part of a growing industry leader, we want to hear from you!
This role is hybrid, requiring occasional office visits in Palo Alto, especially during the initial months.
Benefits
Competitive salary
Equity
Medical, dental, vision insurance
Carrot Fertility Program
Remote work options with co-working budget
Job Details
Senior Level
Full-time
Engineering & IT, Software Development
#J-18808-Ljbffr Remote working/work at home options are available for this role.
AI Tech Lead, Bay Area Hybrid
role at
DataHub .
Find out if this opportunity is a good fit by reading all of the information that follows below. DataHub, built by Acryl Data, is an AI & Data Context Platform adopted by over 3,000 enterprises, including Apple, CVS Health, Netflix, and Visa. It offers metadata graph solutions for enterprise AI and data asset management, with a SaaS offering called DataHub Cloud that enhances AI discovery, observability, and governance.
Role Overview We seek an experienced AI Technical Lead to drive our AI initiatives, focusing on metadata management and AI infrastructure strategy. This role involves technical leadership in AI features and strategic planning for enterprise AI deployment, ensuring governance and efficiency.
Key Responsibilities
Lead the development of AI-powered features such as data classification, PII detection, and sensitive data identification.
Architect scalable ML pipelines for continuous learning and updates.
Design systems for model monitoring, validation, and performance tracking.
Implement privacy-preserving ML techniques and ensure compliance.
Shape metadata frameworks supporting AI systems, including model cards and lineage tracking.
Define standards for AI metadata management, including versioning and deployment configurations.
Develop systems for AI asset management across the development lifecycle.
Establish best practices for AI observability and governance.
Lead architectural decisions for AI system integration.
Mentor team members on ML engineering and AI system design.
Collaborate with product management on AI feature roadmap.
Engage with customers to understand AI infrastructure needs.
Minimum Qualifications
8+ years of software engineering experience, with at least 4 years in ML/AI.
Experience with ML frameworks (PyTorch, TensorFlow) and MLOps tools.
Knowledge of LLM deployment, fine-tuning, and operational considerations.
Experience with AI governance, bias detection, and fairness metrics.
Strong background in data privacy and security in AI.
Experience with enterprise AI deployment and infrastructure management.
Proficiency in Python and AI development tools.
Understanding of vector databases, embedding systems, and semantic search.
Experience with distributed systems and scalable architecture.
Preferred Qualifications
Experience with DataHub is a plus.
Experience building AI features in SaaS products.
Background in data catalog or metadata systems.
Knowledge of AI governance frameworks.
Experience with AI infrastructure cost optimization.
Understanding of AI regulatory requirements.
Track record of production ML systems.
Essential Knowledge Areas
Enterprise AI infrastructure components: model serving, vector databases, training infrastructure, feature stores, model monitoring, governance tools.
Enterprise AI deployment considerations: cost, security, compliance, performance, resource management, versioning.
If you're passionate about technology and customer engagement, and want to be part of a growing industry leader, we want to hear from you!
This role is hybrid, requiring occasional office visits in Palo Alto, especially during the initial months.
Benefits
Competitive salary
Equity
Medical, dental, vision insurance
Carrot Fertility Program
Remote work options with co-working budget
Job Details
Senior Level
Full-time
Engineering & IT, Software Development
#J-18808-Ljbffr Remote working/work at home options are available for this role.