Master's Fellow – Agents, LLMs, Generative AI, Prompt Engineering

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🕒 June 3

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Sistema Fibra

WebsiteLinkedInAll Job Openings

1001 - 5000 employees

Founded 1972

Página oficial do Sistema Fibra

📋 Description

• Understand the current ecosystem of agents, including architecture, tools, logs, memories, execution flows and human feedback mechanisms; • Run at least one existing agent end-to-end in a local/controlled environment; • Map the complete execution flow of an agent and its main interfaces; • Survey the state of the art in agent memory, human feedback, RAG, preference learning and RLHF-inspired approaches; • Select candidate techniques to transform human feedback into memory or operational guidance reusable by agents; • Implement local experiments with controlled examples and/or anonymized real cases; • Create a simulation mechanism and logging for human feedback in the agent's execution loop; • Define evaluation metrics such as success rate, error reduction, stability, latency, cost and comparison against a baseline without adaptive memory; • Integrate the selected approach into at least one pilot agent in the existing ecosystem; • Collect and analyze real interactions with human feedback; • Iterate the solution based on observed results; • Package the solution as a library or reusable component for other agents; • Produce technical documentation, usage examples and a final report detailing results, limitations and next steps.

🎯 Requirements

• Education: Master’s degree • Fields of study: Computer Engineering, Information Systems • Intermediate-level Python • Basic knowledge of LLMs, prompts, agents and using models via APIs • Familiarity with Git, code organization and reading existing projects • Ability to handle structured data, logs, JSON and tables • Basic SQL or willingness to learn quickly • Ability to conduct experiments, define metrics and compare approaches • Good written communication for producing technical documentation and reports • Familiarity with development in real technical environments, including use of APIs, environment variables, authentication, logs and service integration • Experience with agent frameworks such as smolagents, LangChain, LangGraph, CrewAI, AutoGen or similar • Knowledge of RAG, embeddings, vector search or semantic memory • Understanding of evaluation methods for LLMs, agents and human-in-the-loop pipelines • Basic knowledge of reinforcement learning, RLHF, preference learning or adaptive systems • Basic knowledge of AWS or another cloud platform • Familiarity with services such as S3, CloudWatch, IAM, Lambda, ECS/ECR, API Gateway, managed databases, Athena, Secrets Manager, Parameter Store or Amazon Bedrock • Basic notions of deployment, observability, logging, permissions and security in cloud applications • Interest in AI applications in the legal domain.