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Siteup

AI/ML Engineer

Siteup

. Build GenAI/LLM applications: RAG, agentic workflows, chatbots, voice agents, and Bedrock integrations .

Posted 10/6/2026full-timeRemote • BrazilMid-LevelSeniorWebsite

Core Competencies

Role fit
Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in building GenAI and LLM applications, including RAG and chatbots, while leveraging AWS services such as Bedrock and SageMaker. Proficient in MLOps and delivering PoCs and MVPs in a fast-paced environment.

Highest-signal resume keywords
GenAI Application DevelopmentMLOps ImplementationAWS Bedrock and SageMakerAnomaly Detection and ForecastingPoC and MVP Delivery

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills
Machine LearningAnomaly DetectionModel TrainingTime-Series AnalysisPrompt DesignAgent OrchestrationData MigrationForecastingRecommendation EnginesML Infrastructure
Soft Skills
Excellent Communication Skills
Tools & Technologies
AWS LambdaAWS VPCAWS QuickSightDynamoDBS3
Industry Keywords
GenAILLMMLOpsCloud ModernizationData Migration

Tech Stack

Tools & technologies
AWSCloudDynamoDB

About the role

Key responsibilities & impact
  • Build GenAI/LLM applications: RAG, agentic workflows, chatbots, voice agents, and Bedrock integrations
  • Develop classical ML solutions: anomaly detection, forecasting, and recommendation engines
  • Deliver MLOps and ML infrastructure builds
  • Contribute to AWS data and cloud modernization: MAP assessments, QuickSight, and data migrations
  • Take PoCs and MVPs from scoping to delivery within short project timelines
  • Work alongside a Solution Architect and Cloud/Data Engineers on each project

Requirements

What you’ll need
  • 5+ years of experience as an AI/ML Engineer or in a similar role
  • AWS platform depth: Bedrock, SageMaker, Lambda, VPC, QuickSight, DynamoDB/S3
  • GenAI-specific skills: RAG, agent orchestration, guardrails, and prompt design
  • Applied ML fundamentals: time-series, anomaly detection, model training and evaluation
  • Experience with MLOps and ML infrastructure
  • Experience delivering PoCs and MVPs
  • Excellent communication skills in English (B2+)
  • Ability to work independently in a fast-paced, distributed environment