AI Engineer
New
C
CI&TAI Deployment, Tech Solutions
BrazilFull-Time
Salary not disclosed
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Job Details
- Languages
- English
- Required Skills
- PythonPrompt EngineeringGenerative AI
Requirements
- Bachelor's Degree in Computer Science, Engineering, Applied Math, or related fields
- Strong programming background in Python (or similar) with experience in GenAI frameworks and APIs
- Daily use of Generative AI IDEs or environments
- Proven experience in Prompt Engineering, Context Engineering, AI Steering, RAG, MCP (Model Context Protocol) and building agent workflows
- Solid experience with LLMOps, structured Evals, and LLM observability/tracing tools
- Proven knowledge of GenAI Security practices (guardrails, prompt injection mitigation) and secure data integration
- Solid understanding of A2A (Agent to Agent) and ACP (Agent Coordination Protocol)
- Experience in deploying AI-powered solutions across the product development lifecycle
- Understanding of how to integrate short and long-term memory in agents
- Strong communication skills in English, both technical and business-oriented
- Exposure to cloud native environments
- Ability to work independently and collaboratively in fast-paced environments
Responsibilities
- Translate product and engineering challenges into AI-driven solutions that enhance speed, quality, and outcomes
- Build and deploy AI Agents with advanced reasoning, integrating memory, MCP, custom MCP servers, and A2A
- Apply prompt engineering, context engineering, AI steering, RAG, Chain of Thought, ReAct, and other modern AI frameworks to real-world use cases
- Partner with product and engineering teams to embed AI, LLMOps, and observability into requirements, coding, testing, monitoring, and operations
- Prototype, test, optimise, fine-tune, and scale AI solutions, balancing experimentation with production readiness and inference deployment
- Design, run, and automate evals to test LLM outputs for quality, reliability, and safety
- Implement security guardrails and robust data integration across agentic workflows to mitigate vulnerabilities
- Support pre-sales and client discussions by demonstrating applied AI use cases and outcomes
- Stay ahead of research and practice in GenAI and bring them into daily engineering practice
- Communicate findings and trade-offs clearly to both technical teams and executives
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