AI/ MLOps Engineer

DCG is a recruitment company that emphasizes experienced specialists and a partnership management style to effectively meet client needs and provide continuous support. They are currently seeking an AI/MLOps Engineer due to dynamic growth and numerous recruitment projects for their partners.

• Minimum 5 years of professional experience as an IT Developer, MLOps Engineer, Cloud Engineer, or similar technical role.
• Strong practical knowledge of AWS SageMaker and AWS Bedrock.
• Experience with multiple AWS services including Glue, S3, VPC, Step Functions, IAM, X-Ray, Knowledge Bases, CloudWatch, CloudTrail, Glue Catalog, SNS, EventBridge, Lambda, EC2, ECS, Fargate, KMS, MLflow, and Athena.
• Experience creating and managing AWS cloud resources using Terraform.
• Very good knowledge of cloud-native architecture and DevOps principles.
• Experience building, deploying, and operationalizing AI/ML solutions in AWS environments.
• Ability to work in complex cloud environments and support secure and scalable deployments.
• Good communication skills and ability to collaborate effectively with technical and business stakeholders.
• Fluent English language skills, both spoken and written.

• Implement MLOps, GenAI Ops, and Agentic AI Ops frameworks for AI use cases in GFCP.
• Design and configure AWS environments supporting AI/ML workloads.
• Work with AWS SageMaker and AWS Bedrock to enable AI solution creation, deployment, and operationalization.
• Create and manage cloud infrastructure using Terraform.
• Build and maintain automated processes using AWS services such as Step Functions, Lambda, EventBridge, and SNS.
• Support integration of AI/ML solutions with data services including S3, Glue, Glue Catalog, Athena, and Knowledge Bases.
• Ensure appropriate security, monitoring, logging, and supervision using IAM, VPC, KMS, CloudWatch, CloudTrail, and X-Ray.
• Collaborate with technical teams and stakeholders to deliver AI functionalities according to program objectives.
• Support scalable deployment and operational stability of AI solutions in cloud environments.