- Design, build, and optimize robust machine learning models for production environments.
- Implement and automate end-to-end ML pipelines using CI/CD best practices.
- Leverage AWS services for scalable AI infrastructure and model deployment.
- Orchestrate containerized workloads using Kubernetes to ensure high availability and seamless scalability.
- Collaborate with data scientists, engineers, and researchers to translate complex business problems into actionable ML solutions.
- Evaluate, preprocess, and frame real-world data problems for effective machine learning applications.
- Document and communicate findings, solution approaches, and technical decisions with clarity and precision.
Requirements
- Proven expertise in machine learning algorithms, model development, and deployment.
- Strong programming skills, ideally in Python or Java, with experience in large-scale software engineering projects.
- In-depth experience with CI/CD workflows and automation tools.
- Hands-on expertise with AWS cloud services for ML applications and data pipelines.
- Advanced knowledge of Kubernetes for orchestrating containerized ML workloads.
- Exceptional written and verbal communication skills, with a focus on clear technical documentation and team collaboration.
- Demonstrated ability to scope, structure, and solve complex, real-world data challenges.
Application Process
- Easy Apply on LinkedIn
- Participate in resume evaluation & interview stage
#J-18808-Ljbffr