Senior AI Engineer
New
C
CI&TAI engineering
Listing location: Brazil; Workplace type: Remote; Structured job location: BrazilFull-TimeSenior
Salary not disclosed
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Job Details
- Languages
- Advanced or fluent English communication skills (both written and verbal) are essential
- Required Skills
- PythonCI/CDAWS LambdaPrompt Engineering
Requirements
- Bring professional experience in AI engineering, machine learning engineering, or software development with production AI applications.
- Demonstrate strong proficiency in Python.
- Have experience developing APIs, integrations, and automated tests.
- Have hands-on experience building applications with Large Language Models (LLMs) and agentic AI workflows.
- Have experience with prompt engineering, LLM orchestration, and structured output generation.
- Have experience evaluating LLM outputs, implementing testing strategies, and measuring AI application quality.
- Understand LLM limitations, including hallucinations, response variability, and reliability challenges.
- Have experience with AWS services such as Bedrock, Lambda, S3, or CloudWatch.
- Know software engineering best practices, CI/CD, and cloud-based application development.
- Have advanced or fluent English communication skills, both written and verbal.
Responsibilities
- Design, develop, and maintain AI agents and LLM-powered applications using Python and AWS services.
- Implement prompt engineering, agent orchestration, and intelligent decision-making workflows.
- Develop evaluation frameworks to measure agent accuracy, reliability, and behavioral consistency.
- Establish performance baselines, automated tests, and regression validation for changes to agent behavior.
- Implement guardrails and validation mechanisms for safe and reliable AI-generated outputs.
- Develop information retrieval, filtering, ranking, and selection strategies using LLMs.
- Investigate and resolve hallucinations, incorrect interpretations, and inconsistent agent responses.
- Build end-to-end AI features integrating LLMs with backend APIs, document processing, and cloud infrastructure.
- Improve agent observability through execution tracing, session tracking, and production monitoring.
- Collaborate with engineering and product teams and optimize LLM performance, latency, and operational costs.
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