Job Posting Title

MLOps Engineer

Description
Remote work

POSITION OVERVIEW :
You Will:
Focus on model deployments & performance monitoring, training & inference cost optimizations, tech stack upgrades & migrations
Design, improve, and implement best practices in continuous integration and delivery pipelines to speed the rate at which our machine learning engineers can go from ideation to operationalizing machine learning models in a repeatable manner
Setup new model training pipelines ensuring proper tagging, monitoring, alerting, and logging are in place
Deploy projects into a streamlined environment strategy ensuring they can take advantage of best practices
Identify cost savings in areas where computation and storage utilized for model training and inference can be optimized
Support existing day to day requests through our service desk raised by our platform's customers to quickly provide access, unblock issues, and provide general guidance on best platform practices and patterns
Collaborate with existing machine learning engineers, data scientists and ML platform engineers from cross-functional teams

You Bring:
Advanced degree (Master’s or above) in Computer Science, specializing in software engineering or platform engineering
3 years of industry experience working in MLOps position
Strong programming skills in languages such as Python, Scala, and Java
Hands-on Experience in Databricks, mlFlow, Seldon
Excellent problem-solving skills and analytical skills
Deep knowledge of the AWS ecosystem
Expertise in Git, Docker, Kubernetes, Jenkins, Prometheus, and Grafana

Preferred Qualifications:
Experience in supporting as platform engineer, large-scale online customer-facing ML applications, preferably recommendation systems
Experience working with custom ML platforms, feature store, and monitoring ML models
Familiarity with best practices in DevOps, MLOps
Knowledge of additional CI/CD tooling such as Prow, Bazel, and ArgoCD
Experience in Snowflake, Kubeflow, Tecton