Senior MLOps Software Engineer

Typical task breakdown:

· 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

Interaction with team:

- Working with core team, maybe work with additional teams when needed.

- Internal only position

- Working with engineers and scrum teams.

Work environment:

Onsite 2-3 days a week/ no exceptions.

Education & Experience Required:

- Bachelor's degree with 5 years experience

- Master’s degree with 3 years experience

Required Technical Skills

(Required)

· 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.

Nice to Have:

· 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 ClodFormation or Terraform

· Exposure to observability tools (such as Evidently AI)

Soft Skills

(Required)

- Someone who takes the initiative on their own

- Someone who does not need to be micromanage