Design, build, and deliver GenAI models and components that solve complex business problems
Design and implement cloud-native ML Serving Platforms leveraging technologies like Docker, Kubernetes, KNative, and KServe
Solve complex scaling and high-availability problems by writing and testing performant application code in Python and Go-lang
Implement advanced MLOps and GitOps practices for continuous integration and continuous deployment (CI/CD) using tools like ArgoCD
Leverage service mesh architectures like Istio to manage traffic, enhance security, and ensure resilience for high-volume serving endpoints
Retrain, maintain, and monitor models in production
Construct optimized, scalable data pipelines to feed ML models
Ensure all code is well-managed to reduce vulnerabilities and follow best practices in Responsible and Explainable AI

Requirements

Bachelor’s Degree
At least 6 years of experience designing and building data-intensive solutions using distributed computing (Internship experience does not apply)
At least 4 years of experience programming with Python, Scala, Go or Java
At least 2 years of experience building, scaling, and optimizing ML systems
Master's or Doctoral Degree in computer science, electrical engineering, mathematics, or a similar field (Preferred)
3+ years of experience building production-ready data pipelines that feed ML models (Preferred)
3+ years of on-the-job experience with an industry recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow (Preferred)
2+ years of experience developing performant, resilient, and maintainable code (Preferred)
2+ years of experience with data gathering and preparation for ML models (Preferred)
2+ years of people leader experience (Preferred)
1+ years of experience leading teams developing ML solutions using industry best practices, patterns, and automation (Preferred)
Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform (Preferred)
Experience designing, implementing, and scaling complex data pipelines for ML models and evaluating their performance (Preferred)

Benefits

comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being
performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI)

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

PythonGoScalaJavaMLOpsGitOpsCI/CDdata pipelinesML frameworkscloud-native ML Serving Platforms

Soft Skills

people leadershipteam leadershipproblem solvingcommunicationcollaboration

Certifications

Bachelor's DegreeMaster's DegreeDoctoral Degree