remote
Lead Machine Learning Engineer - Faculty
ML Engineer
Lead a team of ML engineers to design, build, and deploy scalable, production‑grade AI solutions using Python, deep‑learning frameworks, and cloud-native infrastructure.
About the role
Key Responsibilities
- Architect and implement end‑to‑end machine‑learning pipelines, from data ingestion to model serving, on cloud platforms.
- Lead a multidisciplinary team, providing technical guidance, code reviews, and mentorship to ensure high‑quality, production‑ready models.
- Collaborate with product, data, and domain experts to translate business problems into scalable AI solutions.
- Design and maintain CI/CD and MLOps workflows using Kubernetes, Docker, and cloud services (AWS) to enable rapid experimentation and reliable deployment.
- Establish best practices for model monitoring, performance tuning, and responsible AI governance.
Requirements
- 5+ years of hands‑on experience building and deploying machine‑learning models in production.
- Strong proficiency in Python and deep‑learning frameworks such as TensorFlow or PyTorch.
- Extensive experience with cloud platforms (AWS) and container orchestration (Kubernetes) for scalable AI services.
- Proven track record leading technical teams and driving end‑to‑end ML projects.
- Solid understanding of MLOps principles, CI/CD pipelines, and model monitoring.
Skills
pythontensorflowpytorchawskubernetesmlops