MLOps Engineer 

UST is hiring an MLOps Engineer to manage ML lifecycle operations and deployment automation. The role focuses on automating model workflows, monitoring production models, collaborating across engineering teams, and establishing governance and security processes for ML platforms.

Responsibilities:

• Automate model training, testing, deployment, and monitoring workflows
• 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

Requirements:

• experience with ML lifecycle management and deployment automation
• Strong Python programming skills
• Hands-on expertise with: o MLflow o Kubeflow o Amazon SageMaker o AWS Step functions o 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
• Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or related field
• Experience taking ML/AI solutions from Proof of Concept (PoC) to Production
• Strong problem-solving and stakeholder management skills
• Hands on experience with AWS
• Knowledge or hands on experience of 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