Senior Staff Engineer, AI

Role Overview: As an AI System Architect, your role will involve leading the design and development of large-scale AI/ML systems from data ingestion to model training, deployment, and monitoring. You will define best practices for model lifecycle management, feature engineering, and MLOps infrastructure. Additionally, you will drive high-impact AI initiatives, stay updated on AI advancements, mentor teams, and ensure timely delivery of AI solutions with measurable outcomes. Key Responsibilities: - Lead the design of large-scale AI/ML systems from data ingestion to model training, deployment, and monitoring. - Define best practices for model lifecycle management, feature engineering, and MLOps infrastructure. - Drive high-impact AI initiatives spanning multiple teams and product lines. - Review and influence the design and implementation of models, APIs, and distributed ML pipelines. - Ensure scalability, performance, and ethical use of AI systems. - Stay updated on AI and machine learning advances, bringing research into production environments. - Mentor senior ML engineers and data scientists on technical best practices and system design. - Partner with product and data teams to translate business challenges into AI-driven solutions. - Lead the technical roadmap for AI systems, ensuring timely delivery of models and services with measurable outcomes. - Establish standards for model testing, explainability, and monitoring. Qualification Required: - 10+ years of experience in software engineering or data science, with at least 3+ years in applied AI/ML leadership roles. - Proven track record of architecting, deploying, and maintaining production-scale ML systems. - Strong programming skills in Python and proficiency with ML frameworks like PyTorch and TensorFlow. - Experience with large-scale data systems and ML pipelines such as Kubeflow, Airflow, Vertex AI, or SageMaker. - Deep understanding of machine learning fundamentals including model training, evaluation, and serving. - Expertise in cloud-based ML infrastructure (AWS, GCP, or Azure) and containerization (Kubernetes, Docker). - Excellent communication skills and ability to influence technical direction across multiple teams. Additional Details: Experience with LLMs, transformer architectures, vector databases, and retrieval-augmented generation (RAG) is a nice-to-have. Understanding of AI safety, model interpretability, and responsible AI principles is also beneficial. Publications, patents, or open-source contributions in AI/ML are considered advantageous. A background in applied domains such as NLP, computer vision, or recommendation systems would be beneficial for this role.,