Enable job alerts via email!

AI Tech Lead, Bay Area Hybrid

DataHub

Palo Alto (CA)

Hybrid

USD 150,000 - 220,000

Full time

30 days ago

Boost your interview chances

Create a job specific, tailored resume for higher success rate.

Job summary

DataHub is seeking an experienced AI Tech Lead to drive AI initiatives focusing on metadata management and infrastructure strategy. This senior role involves leading the development of AI features, mentoring team members, and ensuring compliance with AI governance. The position offers a hybrid work model, competitive salary, and benefits including equity and health insurance.

Benefits

Competitive salary
Equity
Medical, dental, vision insurance
Carrot Fertility Program
Remote work options with co-working budget

Qualifications

  • 8+ years of software engineering experience, with at least 4 years in ML/AI.
  • Experience with ML frameworks and MLOps tools.
  • Strong background in data privacy and security in AI.

Responsibilities

  • Lead the development of AI-powered features such as data classification and PII detection.
  • Architect scalable ML pipelines for continuous learning.
  • Mentor team members on ML engineering and AI system design.

Skills

Machine Learning
AI Governance
Data Privacy
Python
MLOps
Distributed Systems

Tools

PyTorch
TensorFlow

Job description

Join to apply for the AI Tech Lead, Bay Area Hybrid role at DataHub.

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
  1. Lead the development of AI-powered features such as data classification, PII detection, and sensitive data identification.
  2. Architect scalable ML pipelines for continuous learning and updates.
  3. Design systems for model monitoring, validation, and performance tracking.
  4. Implement privacy-preserving ML techniques and ensure compliance.
  5. Shape metadata frameworks supporting AI systems, including model cards and lineage tracking.
  6. Define standards for AI metadata management, including versioning and deployment configurations.
  7. Develop systems for AI asset management across the development lifecycle.
  8. Establish best practices for AI observability and governance.
  9. Lead architectural decisions for AI system integration.
  10. Mentor team members on ML engineering and AI system design.
  11. Collaborate with product management on AI feature roadmap.
  12. Engage with customers to understand AI infrastructure needs.
Minimum Qualifications
  1. 8+ years of software engineering experience, with at least 4 years in ML/AI.
  2. Experience with ML frameworks (PyTorch, TensorFlow) and MLOps tools.
  3. Knowledge of LLM deployment, fine-tuning, and operational considerations.
  4. Experience with AI governance, bias detection, and fairness metrics.
  5. Strong background in data privacy and security in AI.
  6. Experience with enterprise AI deployment and infrastructure management.
  7. Proficiency in Python and AI development tools.
  8. Understanding of vector databases, embedding systems, and semantic search.
  9. Experience with distributed systems and scalable architecture.
Preferred Qualifications
  1. Experience with DataHub is a plus.
  2. Experience building AI features in SaaS products.
  3. Background in data catalog or metadata systems.
  4. Knowledge of AI governance frameworks.
  5. Experience with AI infrastructure cost optimization.
  6. Understanding of AI regulatory requirements.
  7. Track record of production ML systems.
Essential Knowledge Areas
  1. Enterprise AI infrastructure components: model serving, vector databases, training infrastructure, feature stores, model monitoring, governance tools.
  2. 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
Get your free, confidential resume review.
or drag and drop a PDF, DOC, DOCX, ODT, or PAGES file up to 5MB.