Job Description

Salary: $82,000 - 122,000 per year Requirements:

• Bachelors degree in Computer Science, Engineering, Data Science, or a related discipline, or equivalent experience
• At least 3 years of experience in cloud engineering, ML engineering, or DevOps, including 2 or more years working hands-on with AWS
• Strong Python and SQL skills
• Practical experience with SageMaker and core AWS services, such as S3, EC2, IAM, VPC, Lambda, ECR, and CloudWatch
• Experience with Docker, Kubernetes (preferably EKS), infrastructure as code (Terraform, CloudFormation, or CDK), and Git-based CI/CD tools such as GitHub Actions, GitLab CI, or CodePipeline
• Understanding of the machine learning lifecycle, including training, evaluation, deployment, and monitoring
• Preferred: AWS certification in Machine Learning Specialty, Solutions Architect, or DevOps Engineer
• Preferred: Experience with computer vision and intelligent document processing on AWS, including Rekognition or Textract
• Preferred: Experience with Amazon Bedrock, generative AI, LLM fine-tuning, or RAG systems
• Preferred: Experience with streaming and big data tools such as Kinesis, Kafka, Spark, or EMR
• Preferred: Familiarity with model monitoring and explainability tools such as SageMaker Model Monitor, Clarify, or MLflow
• Preferred: Experience in regulated or public-sector environments, including FedRAMP, GovCloud, or other compliance frameworks
• Preferred: Knowledge of GPU workloads, distributed training, and inference optimization

• Design and deploy AWS environments for machine learning training and inference using SageMaker, Bedrock, and related services
• Build and maintain scalable data and ML pipelines with tools such as SageMaker Pipelines, Step Functions, Airflow/MWAA, and Glue
• Establish MLOps practices for model versioning, registries, automated retraining, CI/CD, and drift and performance monitoring
• Provision cloud infrastructure as code using Terraform, CloudFormation, or AWS CDK
• Deploy models through REST APIs or serverless endpoints using services such as API Gateway, Lambda, ECS/EKS, and SageMaker endpoints
• Integrate foundation models and RAG architectures with Bedrock, OpenSearch, and vector databases
• Apply security and governance controls, including least-privilege IAM, VPC design, encryption, secrets management, and audit logging
• Monitor and improve cost, latency, and reliability using CloudWatch, X-Ray, Cost Explorer, and related tools
• Containerize workloads with Docker and orchestrate them with Kubernetes, including EKS
• Document architectures and guide teammates on cloud and MLOps best practices

• AI
• Airflow
• API
• AWS
• Lambda
• OpenSearch
• Architect
• Big Data
• CI/CD
• Cloud
• CloudWatch
• Computer Vision
• DevOps
• Docker
• EC2
• Fine-tuning
• Git
• GitHub
• GitLab
• IAM
• Kafka
• Kubernetes
• LLM
• Machine Learning
• MLflow
• MLOps
• Python
• RAG
• REST
• SQL
• Security
• Serverless
• Spark
• Terraform

More:

We are seeking an ML Cloud AWS Engineer for an onsite contract assignment with our client in Austin, Texas. In this role, youll help move machine learning models from prototype to production by building and operating cloud infrastructure and pipelines on AWS. Youll collaborate with data scientists, software engineers, and security teams to deliver scalable, secure, and cost-efficient machine learning and generative AI solutions.

last updated 40 week of 2026

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