Senior AI Architect - GenAI & Recommendation Systems

As an AI Architect (Data Science) at Blend, you will play a key role in leading the development of cutting-edge machine learning solutions that drive business impact. Your responsibilities will include strategic leadership, team management, custom transformer architecture design, model development, data science workflows, client engagement, MLOps implementation, infrastructure setup, and governance. Here is a detailed breakdown of what will be expected from you: Role Overview: In this senior role, you will be responsible for owning the full lifecycle of AI model development, setting technical strategy, and ensuring the successful transition of machine learning solutions from concept to production with a focus on business impact. Key Responsibilities: Strategic Leadership & Team Management: - Define technical investments aligned with business objectives. - Mentor and manage AI/ML engineers, senior data scientists, and MLOps engineers. - Partner with cross-functional leaders to prioritize initiatives and measure organizational impact. - Establish engineering standards, code review practices, and model governance frameworks. Custom Transformer Architecture & Model Development: - Lead the design and development of custom transformer models for various applications. - Drive innovation in attention mechanisms, positional encodings, and tokenization strategies. - Adapt and fine-tune foundation models for proprietary client datasets. - Champion reproducible experimentation and architectural decision documentation. Data Science & Applied Analytics: - Oversee end-to-end data science workflows and ensure statistical rigor in experimental design. - Guide the team in building robust data pipelines for structured and unstructured datasets. Client & Executive Engagement: - Lead technical discovery with enterprise clients and present AI strategy to senior stakeholders. - Contribute to business development efforts by supporting RFP responses and client proposals. MLOps, Infrastructure & Governance: - Establish production standards for model deployment, monitoring, and drift detection. - Drive adoption of MLOps best practices including CI/CD for ML and model governance. - Implement model explainability and responsible AI standards. Qualifications: - Bachelors or Masters degree in Computer Science, Statistics, Mathematics, or related field; Ph.D. preferred. - 10+ years of experience in data science and machine learning, with people management experience. - Hands-on expertise in designing and training custom transformer architectures. - Proficiency in Python and core ML/DL libraries. - Experience with industry datasets in marketing & media or telecommunications. - Strong SQL and large-scale data platform skills. - End-to-end MLOps experience and exceptional executive communication skills. This is a high-impact, high-autonomy role where you will have the opportunity to define the AI roadmap, establish best practices, and drive innovation in AI engineering at Blend. As an AI Architect (Data Science) at Blend, you will play a key role in leading the development of cutting-edge machine learning solutions that drive business impact. Your responsibilities will include strategic leadership, team management, custom transformer architecture design, model development, data science workflows, client engagement, MLOps implementation, infrastructure setup, and governance. Here is a detailed breakdown of what will be expected from you: Role Overview: In this senior role, you will be responsible for owning the full lifecycle of AI model development, setting technical strategy, and ensuring the successful transition of machine learning solutions from concept to production with a focus on business impact. Key Responsibilities: Strategic Leadership & Team Management: - Define technical investments aligned with business objectives. - Mentor and manage AI/ML engineers, senior data scientists, and MLOps engineers. - Partner with cross-functional leaders to prioritize initiatives and measure organizational impact. - Establish engineering standards, code review practices, and model governance frameworks. Custom Transformer Architecture & Model Development: - Lead the design and development of custom transformer models for various applications. - Drive innovation in attention mechanisms, positional encodings, and tokenization strategies. - Adapt and fine-tune foundation models for proprietary client datasets. - Champion reproducible experimentation and architectural decision documentation. Data Science & Applied Analytics: - Oversee end-to-end data science workflows and ensure statistical rigor in experimental design. - Guide the team in building robust data pipelines for structured and unstructured datasets. Client & Executive Engagement: - Lead technical discovery with enterprise clients and present AI strategy to senior stakeholders. - Contribute to business development effor