AWS Platform Engineer (MLOps)

Role: MLOps Engineer AWS SageMaker, Terraform Location: Remote Duration: 6+ months Required Skills 4+ years MLOps/ML Engineering with 2+ years hands-on AWS SageMakerTraining jobs, Processing jobs, Pipelines, Model Registry, Endpoints, Clarify, Model MonitorS3, ECR, Lambda, Step Functions, IAM, VPC, CloudWatch, EventBridgeDocker, building custom SageMaker containers, inference.py/entrypoint scriptsBoto3, SageMaker SDK, pandas, ML frameworks XGBoost, Sklearn, PyTorch, TensorFlowCI/CD & IaC: Git, C

Experience: [48]years software engineering, including [2]+ building with LLMs Role summary The AI Engineer builds the application layer. This is the hands-on-keyboard role that turns an architecture into a working, integrated, production-grade AI system: RAG pipelines, agents, guardrails, and the integration that embeds them into client systems. The engineer must write code that runs unchanged in intent whether the model sits behind an Azure OpenAI endpoint, an AWS Bedrock endpoint, or a NIM mic

Forward Deployment Engineer AWS AI/ML Consulting DEPARTMENT Consulting LEVEL Senior LOCATION Remote TYPE 40 hrs About the RoleAs anAWSForward Deployment Engineer (FDE), you will sit at the intersection of applied AI engineering and hands-on customer partnership. You will embed directly with our most strategic enterprise customers to design, prototype, and deliver production-grade AI solutions onAWS building agentic systems with Amazon Bedrock, generative AI frameworks, and advanced orchestration

Job Title: AI/ML Engineer (Remote)Location: RemoteExperience: 5+ YearsSalary: 90,000 1,80,000+ per month (Based on experience and skills)Employment Type: Full-Time About the RoleWe are looking for an experienced AI/ML Engineer to design, develop, and deploy scalable machine learning and artificial intelligence solutions. The ideal candidate should have hands-on experience building ML models, working with large datasets, deploying AI applications, and collaborating with cross-functional teams to