MACHINE LEARNING (ML) OPS ENGINEER - 74407

Executive Service

This position may be eligible for a hybrid work schedule.

Background Check:

Will require CJIS/FTI Fingerprints and name-based background check. This position requires a criminal background check. Therefore, you may be required to provide information about your criminal history in order to be considered for this position.

Who we are and what we do:

The MLOps Engineer will design, implement, and maintain scalable and resilient solutions to operationalize AI and ML capabilities. This role focuses on developing automated ML pipelines, managing infrastructure, and ensuring best practices in AI security and observability. The engineer will collaborate closely with data scientists and engineering teams to deliver production-ready solutions while staying current with emerging technologies and frameworks.

How you make a difference in this role:

See Key Responsibilities

Key Responsibilities:

1. Develop, productionize, and deploy scalable software solutions to operationalize AI/ML capabilities.
2. Build, document, and maintain automated end-to-end ML pipelines for model training, evaluation, tracking, and deployment.
3. Design & implement feature engineering workflows for data extraction and transformation.
4. Implement monitoring, observability, and AI security best practices across ML systems.
5. Manage and document lab infrastructure, resources, tool integrations, and user permissions.
6. Collaborate with data scientists to provision environments and ensure compliance with governance standards.
7. Stay up to date with ML frameworks, MLOps tools, and LLMOps technologies to advocate for industry best practices.

Minimum Qualifications:

1. Experience with LLM platforms (e.g., Bedrock) and frameworks (e.g., LangChain, LangFuse, etc.)
2. Experience with popular MLOps tools (e.g., Sagemaker), and frameworks (e.g.: TensorFlow, Keras, Theano, PyTorch, Caffe, etc.)
3. Proficiency in deploying secure, scalable, production solutions on AWS including resource provisioning, connectivity, security, autoscaling, and creating Infrastructure as Code via tools such as CloudFormation.
4. Understanding of LLMs, and supporting concepts (tokenization, guardrails, chunking, Retrieval Augmented Generation, etc.).
5. Knowledge of ML lifecycle (wrangling data, model selection, model training, modeling validation and deployment at scale) and experience working with data scientists.
6. Hands-on experience with CI/CD, version control, and test-driven development using tools like Azure DevOps or CircleCI. Strong knowledge of ML lifecycle and experience working with data scientists.

Preferred Skills:

1. Familiarity with cloud data warehousing (Snowflake, Redshift). Education & Experience: Bachelors degree in Computer Science, Engineering, or related field (or equivalent experience).
2. Minimum 2 years of experience in MLOps, DevOps, or related fields (4+ years preferred).

Pursuant to the State of Tennessee's Workplace Discrimination and Harassment policy, the State is firmly committed to the principle of fair and equal employment opportunities for its citizens and strives to protect the rights and opportunities of all people to seek, obtain, and hold employment without being subjected to illegal discrimination and harassment in the workplace. It is the State's policy to provide an environment free of discrimination and harassment of an individual because of that person's race, color, national origin, age (40 and over), sex, pregnancy, religion, creed, disability, veteran's status or any other category protected by state and/or federal civil rights laws.