Senior MLOps Software Engineer

Education Requirements:
- Bachelors degree with 5  years experience
- Master’s degree with 3  years experience

Required Skills for the Senior MLOps Software Engineer:
- 5  years of experience working with an object-oriented programming language (Python, Golang, Java, C/C  etc.)
- Experience with MLOps frameworks like MLflow, Kubeflow, etc.
- Proficiency in programming (Python, R, SQL)
- Ability to design and implement cloud solutions and build MLOps pipelines on cloud solutions (e.g., AWS)
- Strong understanding of DevOps principles and practices, CI/CD, etc. and tools (Git, GitHub, jFrog Artifactory, Azure DevOps, etc.)
- Experience with containerization technologies like Docker and Kubernetes
- Strong communication and collaboration skills
- Ability to help work with a team to create User Stories and Tasks out of higher-level requirements.

Preferred Skills:
- Ability to create model inference systems with advanced deployment methods that integrate with other MLOps components like MLFlow.
- Knowledge of inference systems like Seldon, Kubeflow, etc.
- Knowledge of deploying applications and systems in Langfuse or Kubernetes using Helm and Helmfile.
- Knowledge of infrastructure orchestration using CloudFormation or Terraform
- Exposure to observability tools (such as Evidently AI)

Senior MLOps Software Engineer Overview:
The MLOps Platform Team works within the Enterprise Data and Analytics Organization, driving the ability to work with internal Teams to be able to support the full life-cycle of AI and machine learning development through to beyond production.  Helping build a platform that enables data driven decisions across the enterprise, helping teams build high-value data and AI/ML products, and enable the operationalization and reliability of all models.  The role will build the MLOps Platform, build self-service ML Development tooling, and building platform adoption. Y

Responsibilities:
- Define scalable and secure architectures, frameworks and pipelines for building, deploying and diagnosing production ML applications
- Enable users & teams on the ML platform; troubleshoot and debug user issues; maintain user-friendly documentation and training.
- Collaborate with internal stakeholders to build a comprehensive MLOps Platform
- Design and implement cloud solutions and build MLOps pipelines on cloud solutions (e.g., AWS)
- Develop standards and examples to accelerate the productivity of data science teams. 
- Run code refactoring and optimization, containerization, deployment, versioning, and monitoring of its quality, including data & concept drift
- Create way to automate the testing, validation, and deployment of data science models
- Provide best practices and execute POC for automated and efficient MLOps at scale