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Job Description
Structured overview of role & requirementsAbout This Role
Lead architecture and development of scalable ML infrastructure, including model training, evaluation, deployment, and monitoring systems.
Optimize ML systems for reliability, performance, and maintainability across production environments.
Mentor junior ML engineers and drive best practices in MLOps, automation, and software quality within the team.
Minimum Requirements
5-6 years industry experience in ML engineering, backend engineering, or ML infrastructure roles.
Proficient in Python and at least one systems-level programming language (Go, Java, or C++).
Experience with ML infrastructure components such as model registries, training orchestration, and distributed data pipelines.
Familiarity with containerization and cloud deployment tools (Docker, Kubernetes, AWS SageMaker, Vertex AI) and MLOps frameworks (MLflow, TFX, Kuberflow).
Ideal Candidate Profile
Experienced technical leader capable of driving cross-functional ML platform initiatives and codebase quality.
Skilled at bridging ML research and production by building robust, scalable ML operations systems.
Proven mentor able to advance junior engineers and establish engineering standards inside ML teams.