MLOPS Engineer

Accord Technologies Inc. is seeking an MLOps Engineer for a 12+ month W2 contract in Chicago, IL. The role involves building and supporting the MLOps Platform for ServiceNow's Enterprise Data and Analytics Organization. Responsibilities include defining scalable architectures, enabling users on the ML platform, and designing cloud-based MLOps pipelines.

• 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.

• Bachelor's plus 9+ years of experience, Master's plus 6+ years of experience.
• 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.

• 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).