About Brightly Software
Brightly Software is a leader in intelligent asset management and operational optimization, empowering organizations with data‑driven insights. As we expand our AI and ML capabilities, we are seeking a Senior MLOps Engineer to build and scale the infrastructure that powers our next generation of predictive and autonomous solutions.
Role Overview
As a Senior MLOps Engineer, you will architect, develop, and operate end‑to‑end machine learning infrastructure on AWS. You will work at the intersection of ML engineering, cloud infrastructure, and developer productivity—enabling Brightly's data science teams to move seamlessly from experimentation to reliable, secure, and cost‑efficient production systems.
Your work will ensure that ML models and data pipelines are scalable, observable, and compliant with best‑in‑class MLOps practices.
Key Responsibilities
ML Platform \& Infrastructure (AWS‑focused)* Design, build, and operate ML/AI development platforms on AWS, leveraging services such as Amazon SageMaker (Studio, Training, Real‑Time \& Async Inference, Pipelines, Feature Store), S3, Glue, Lambda, ECS/EKS, and related cloud infrastructure.
Data \& Model Pipelines* Build automated data ingestion and transformation pipelines using S3, Glue, EMR/Spark, and Redshift, incorporating data quality and lineage tooling (e.g., Great Expectations, Deequ).
CI/CD for Machine Learning* Develop CI/CD pipelines for ML with CodeBuild, CodePipeline, or GitHub Actions, integrating unit tests, data contract checks, model validation, canary/shadow deployments, and automated rollback strategies.
Model Deployment \& Operations* Deploy real‑time inference endpoints (SageMaker endpoints or FastAPI‑based services on Lambda/ECS/EKS) and scalable batch processing jobs.
Monitoring, Observability \& Governance* Implement production monitoring for drift, bias, and performance using SageMaker Model Monitor and service telemetry tools like CloudWatch, Prometheus, and Grafana.
Cross‑Functional Collaboration* Partner closely with data scientists, ML engineers, and backend engineers to productionize ML models and streamline development workflows.
Required Qualifications* Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
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