Machine Learning Operations Specialist  

Capability Requirements
● Execute performance tuning activities for model serving infrastructure to maintain optimal latency and throughput.
● Conduct post-deployment validation checks to ensure model prediction stability, API responsiveness, and overall service quality.
● Support the enhancement of operational pipelines, including CI/CD workflows, configuration templates, and automated monitoring scripts.
● Participate in service reliability reviews to improve platform uptime, incident response processes, and operational readiness.
● Coordinate closely with DevOps and Platform Engineering to address infrastructure-level concerns related to model hosting and deployment.
● Assist in the rollout of platform-level improvements, including model registry enhancements, container optimization, and new monitoring tools.

Related Work Experience:

• Minimum of 2+ years hands-on experience in a production environment covering MLOps, Data Engineering, or Software Engineering.
• Demonstrated ability to meet and exceed strict Service Level Agreements (SLAs), especially those related to system uptime, stability, incident response, and resolution.
• Experience supporting cloud-hosted ML systems in distributed, high-availability environments.

● Knowledge:

• Strong understanding of model deployment workflows, including model versioning, serving, rollout strategies, and post-deployment validation.
• Knowledge of cloud platforms (e.g., AWS Cloud) and their native ML services used for hosting, monitoring, and managing model endpoints.
• Familiarity with containerization (Docker) and orchestration (Kubernetes) for scalable ML serving infrastructure.
• Understanding of performance monitoring concepts, including latency tracking, model health indicators, and drift signals.
• Knowledge of CI/CD processes, configuration templates, and automated operational workflows specific to ML systems.

● Skills:

• Proven expertise in MLOps, specifically managing model deployment, proactive monitoring, incident resolution, and performance tuning.
• Ability to write and maintain automation scripts, validation utilities, and operational workflows to support ML pipelines.
• Ability to collaborate effectively with DevOps, Data Science, and Platform Engineering teams to improve model reliability and system stability.
• Skilled in applying structured software development methodologies (e.g., Agile/Scrum) to support platform enhancements and iterative delivery.
• Strong analytical, troubleshooting, and root-cause diagnosis skills in production environments.