ML Cloud AWS Engineer

We are seeking an ML Cloud AWS Engineer for an Onsite Contract assignment with our Austin, Texas client to design, build, and operate the cloud infrastructure and pipelines that take machine learning models from prototype to production on AWS. You will work with data scientists, software engineers, and security teams to deliver scalable, secure, and cost-efficient ML and generative AI solutions.

Key Responsibilities
* Design and deploy ML training and inference environments on AWS using Sage Maker, Bedrock, and related services.
* Build and maintain scalable data and ML pipelines (Sage Maker Pipelines, Step Functions, Airflow/MWAA, Glue).
* Implement MLOps practices: model versioning, registries, automated retraining, CI/CD, and monitoring for drift and performance.
* Provision infrastructure as code using Terraform, Cloud Formation, or AWS CDK.
* Deploy models as REST APIs or serverless endpoints (API Gateway, Lambda, ECS/EKS, Sage Maker endpoints).
* Integrate foundation models and RAG architectures using Bedrock, Open Search, and vector databases.
* Enforce security and governance: IAM least-privilege, VPC design, encryption, secrets management, audit logging.
* Monitor and optimize cost, latency, and reliability (Cloud Watch, X-Ray, Cost Explorer).
* Containerize workloads with Docker and orchestrate with Kubernetes (EKS).
* Document architectures and mentor teammates on cloud and MLOps best practices. Requirements
* Bachelor's degree in Computer Science, Engineering, Data Science, or a related field (or equivalent experience).
* 3+ years of experience in cloud engineering, ML engineering, or Dev Ops, with 2+ years hands-on in AWS.
* Strong Python and SQL skills.
* Hands-on experience with Sage Maker and core AWS services (S3, EC2, IAM, VPC, Lambda, ECR, Cloud Watch).
* Experience with Docker and Kubernetes (EKS preferred).
* Experience with infrastructure as code (Terraform, Cloud Formation, or CDK).
* Experience with CI/CD tools (Git Hub Actions, Git Lab CI, Code Pipeline) and Git.
* Understanding of the ML lifecycle: training, evaluation, deployment, and monitoring. Preferred
* AWS certifications (Machine Learning Specialty, Solutions Architect, or Dev Ops Engineer).
* Experience with computer vision and intelligent document processing on AWS (Rekognition, Textract).
* Experience with Amazon Bedrock, generative AI, LLM fine-tuning, or RAG systems.
* Experience with streaming and big data tools (Kinesis, Kafka, Spark, EMR).
* Familiarity with model monitoring and explainability tools (Sage Maker Model Monitor, Clarify, MLflow).
* Experience in regulated or public-sector environments (FedRAMP, Gov Cloud, compliance frameworks).
* Knowledge of GPU workloads, distributed training, and inference optimization.