Distinguished AI Engineer

Role overview

Core responsibilities

• Define AI/ML architecture and technical vision; design, develop, and deploy scalable AI systems.
• Lead machine learning, deep learning, generative AI, NLP, computer vision, recommendation, and predictive-system development as applicable.
• Evaluate emerging research and technologies; optimize systems for performance, reliability, scalability, and cost.
• Integrate LLMs, foundation models, AI agents, and automation; establish model development, evaluation, deployment, monitoring, and lifecycle practices.
• Mentor engineering teams, resolve complex challenges, and develop efficient inference, latency, and resource-use strategies.
• Build data pipelines, feature engineering, training workflows, and MLOps infrastructure; establish quality, security, governance, and responsible AI practices.
• Set engineering standards, measure model quality and impact, take proof-of-concepts to production, and develop AI roadmaps with executives.
• Support research, publications, patents, intellectual property, and monitoring of emerging AI fields as applicable.

Required qualifications

• Extensive professional experience in AI engineering, machine learning, deep learning, software engineering, or a related technical field; proven record delivering complex AI/ML systems at scale.
• Deep expertise in AI/ML architectures, algorithms, and engineering practices; strong programming skills in Python, C++, Java, or similar languages.
• Extensive experience with PyTorch, TensorFlow, JAX, Hugging Face, or similar frameworks.
• Experience with cloud platforms and production MLOps, including deployment, monitoring, versioning, and AI infrastructure.
• Knowledge of distributed systems, data pipelines, APIs, microservices, containers, and scalable software architecture.
• Strong understanding of AI evaluation, model optimization, responsible AI, security, and governance; excellent problem-solving and systems thinking.
• Demonstrated technical influence, communication, leadership, and mentoring skills.

Preferred qualifications

• Understanding of LLMs, generative AI, transformers, fine-tuning, RAG, AI agents, or other modern AI technologies is preferred.
• A PhD or master's degree in a related technical field is preferred.
• Contributions to AI research, patents, publications, open source, or recognized industry innovation are advantageous.

Opportunity and benefits

• Opportunity to shape advanced AI initiatives and work with executive, research, product, and engineering teams.
• The posting describes access to advanced computing, AI platforms, research opportunities, conferences, and professional development.
• The posting mentions executive-level compensation, performance incentives, equity opportunities, and comprehensive benefits without stating amounts.