The Senior AI Engineer is a senior individual contributor with deep expertise in a specific domain of AI/ML, responsible for tackling the most complex technical challenges, driving architectural decisions for critical AI components, and setting technical standards. This role requires a blend of advanced research understanding and robust engineering skills to bring cutting-edge AI innovations into production.
The difference you will make
- Act as a subject matter expert in one or more specialized AI/ML domains (e.g., Generative AI, Explainable AI, Large Language Models, Reinforcement Learning, Real-time Anomaly Detection).
- Lead the design and implementation of highly scalable, fault-tolerant, and high-performance AI systems and infrastructure.
- Evaluate and integrate new AI/ML research, algorithms, and technologies into product development.
- Drive continuous improvement in MLOps, model monitoring, and model governance frameworks.
- Provide expert technical guidance and mentorship to other AI Engineer 1s and Associates.
- Conduct complex performance tuning, optimization, and debugging of AI/ML models and pipelines.
- Participate in strategic planning for AI initiatives, identifying opportunities for technological advancement.
- Influence technical direction and architecture discussions within the AI/ML team and across engineering.
- Lead code reviews for critical components, ensuring architectural integrity, performance, and maintainability.
Requirements
Education:
Bachelor’s degree or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a closely related quantitative field.
Experience:
- 5+ years of progressive experience in AI/ML engineering, with a proven track record of designing and deploying complex AI systems into production.
- Demonstrated deep expertise in a specific AI sub-field.
Technical Skills:
- Expert-level proficiency in Python and advanced ML/DL frameworks (e.g., PyTorch, TensorFlow, JAX).
- Extensive experience with cloud-native AI/ML services and distributed computing frameworks (e.g., Spark, Ray).
- Deep understanding and hands-on experience with MLOps best practices, including CI/CD, model serving, and monitoring in production environments.
- Strong architectural design skills for complex data pipelines and scalable AI inference systems.
- Proficiency in system design, distributed systems, and performance optimization.
- Ability to critically evaluate and apply academic research to real-world problems.
Soft Skills:
- Strong ownership and accountability for assigned work.
- Clear and professional verbal and written communication.
- Effective time management and task prioritization. o Analytical thinking and problem-solving skills.
- Ability to work independently, collaborate effectively and mentor junior team members.
- Ability to actively listen to customers and accurately understand their needs, expectations, and challenges.
- Adaptability and learning agility in a fast-paced environment.
- Strong attention to detail and a commitment to quality.
- Receptiveness to feedback and continuous improvement mindset
Tags & focus areas
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