MLOps Engineer at SFORCE IT

About the role

Implement model versioning, experiment tracking, and model registry solutions. Monitor model performance, drift, and operational health in production environments. Collaborate with Data Engineers and DevOps teams to operationalize ML solutions. Establish governance, security, access control, and auditability processes for ML platforms. This description outlines the typical responsibilities and tasks for the position; additional essential functions may be required.

Responsibilities

• Implement model versioning, experiment tracking, and model registry solutions.
• Monitor model performance, drift, and operational health in production environments.
• Collaborate with Data Engineers and DevOps teams to operationalize ML solutions.
• Establish governance, security, access control, and auditability processes for ML platforms.

Required Skills

• Strong Python programming skills.
• Experience with ML lifecycle management and deployment automation.
• Hands‑on expertise with:
  • MLflow
  • Kubeflow
  • Amazon SageMaker
  • AWS Step Functions
  • ECS (Elastic Container Services)
• Knowledge of Docker and Kubernetes.
• Experience with CI/CD tools and DevSecOps practices.
• Familiarity with Terraform, CloudFormation, or similar IaC tools.
• Understanding of model monitoring, observability, and performance optimization.

Preferred Skills

• Hands‑on experience with AWS.
• Knowledge or hands‑on experience with Agentcore.
• Knowledge of data‑engineering tools such as Databricks, Spark, Airflow, Kafka, or Snowflake.
• Understanding of Responsible AI, model governance, and compliance requirements.
• Exposure to Generative AI, LLMOps, and RAG‑based solutions.

Qualifications

• Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or a related field.
• Experience taking ML/AI solutions from Proof of Concept (PoC) to production.
• Strong problem‑solving and stakeholder management skills.