MLOps Engineer

Construct, maintain, and upgrade automated data pipeline structures to support production-scale AI services.
Deploy, scale, and manage machine learning workloads effectively using AWS cloud environments.
Build enterprise-grade ML pipelines and tracking configurations using tools like Amazon SageMaker.
Execute rigorous model validation, drift detection, and performance benchmarking protocols before production shifts.
Collaborate actively across cross-functional data science and software groups to streamline AI application delivery.
Optimize cloud infrastructure configurations to ensure optimal cost-performance indices across computing clusters.
Participate productively within a hybrid work environment following standard agile operational timeframes.

Degree in Computer Science, Information Technology, Business Analytics, or a related quantitative discipline.
3-5 years of professional technology experience, featuring clear growth inside production DevOps or MLOps positions.
Proven track record deploying complex machine learning frameworks and handling enterprise validation routines.
Deep structural knowledge of AWS systems, automation scripts, continuous delivery structures, and code versioning.
Excellent communication and time-management skills tailored for high-impact hybrid execution teams.