AI Technical Lead, Acryl Data

• 8+ years of software engineering experience, with at least 4 years focused on ML/AI systems
• Strong experience with modern ML frameworks (PyTorch, TensorFlow) and MLOps tools
• Deep understanding of LLM deployment, fine-tuning, and operational considerations
• Experience with AI governance, including model monitoring, bias detection, and fairness metrics
• Strong background in data privacy and security, particularly in AI contexts
• Experience with enterprise AI deployment and infrastructure management
• Proficiency in Python and modern AI development tools
• Understanding of vector databases, embedding systems, and semantic search
• Experience with distributed systems and scalable architecture
• Deep understanding of enterprise AI infrastructure components:
• Model serving platforms
• Vector databases
• Training infrastructure
• Feature stores
• Model monitoring systems
• AI governance tools
• Familiarity with key considerations for enterprise AI deployment:
• Cost optimization strategies
• Security requirements
• Compliance considerations
• Performance monitoring
• Resource management
• Model versioning and rollback strategies

Desirable

• We're seeking an experienced AI Technical Lead to spearhead our AI initiatives within DataHub, focusing on intelligent metadata management and shaping our AI infrastructure strategy
• This role combines hands-on technical leadership in implementing AI-powered features with strategic thinking about how enterprises deploy and manage AI systems at scale
• You'll work at the intersection of data catalog systems and modern AI infrastructure, helping organizations navigate the complexities of enterprise AI deployment while ensuring robust governance and efficiency
• Lead the technical implementation of AI-powered features in DataHub, including automated data classification, PII detection, and sensitive data identification
• Architect and implement scalable ML pipelines for continuous learning and model updates
• Design and implement systems for model monitoring, validation, and performance tracking
• Guide the team in implementing privacy-preserving ML techniques and ensuring compliance with data protection standards
• Shape the metadata framework needed to support enterprise AI systems, including model cards, lineage tracking, and deployment metadata
• Define standards for capturing and managing AI-related metadata, including training data versioning, model provenance, and deployment configurations
• Design systems to track and manage AI assets across the development lifecycle
• Develop best practices for AI observability and governance in enterprise settings
• Lead architectural decisions for AI systems integration within DataHub
• Mentor team members on ML engineering best practices and AI system design
• Collaborate with product management to define AI feature roadmap
• Work with customers to understand their AI infrastructure needs and challenges