
Chief AI Architect
9 hours ago
Project Lead
The ideal candidate is a seasoned professional with 10-15 years of experience in leading AI/ML projects from concept to execution.
Key Responsibilities:
- Project Vision: Define clear objectives, roles, responsibilities, and project scope for AI/ML initiatives.
- Technical Oversight: Provide hands-on technical guidance, conduct code and solution reviews, and manage the development of AI/ML systems.
- Team Management: Supervise and mentor technical teams, fostering a collaborative environment for AI and data engineering.
- Strategic Planning: Develop and oversee project requirements, schedules, and roadmaps for AI/ML initiatives, aligning with business goals.
- Cross-functional Collaboration: Work closely with product management, data science, and engineering teams to define project strategies and integrate AI into products.
- Technical Expertise: Drive innovation in areas like foundation models, Generative AI (GenAI), Retrieval-Augmented Generation (RAG), and multi-modal LLMs.
- Production Deployment: Oversee the build-out of end-to-end ML systems for production, focusing on scalability, performance, and reliability.
- Responsible AI Practices: Maintain a strong focus on responsible, transparent, and ethical AI practices throughout the project lifecycle.
- Performance Monitoring: Track project progress, key performance indicators (KPIs), and provide status reports to stakeholders and leadership.
Required Skills & Expertise:
- AI Fundamentals: Deep understanding of Machine Learning (ML), Deep Learning, and Natural Language Processing (NLP) concepts.
- Programming & Data: Proficiency in Python, SQL, and data manipulation libraries such as Pandas and NumPy.
- AI Technologies: Experience with Large Language Models (LLMs), foundation models, RAG, and prompt engineering.
- Tools & Platforms: Familiarity with AI experiment tracking tools like Weights & Biases and configuration management tools like Hydra.
- Leadership & Communication: Proven ability to lead and inspire technical teams, communicate technical concepts to diverse stakeholders, and align AI initiatives with business objectives.
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