Solutions
Recruitment Software
Post jobs, import profiles, add screening questions, and manage hiring all in one place with personalized questions and control the entire recruitment process.
Recruitment Service
Let our expert team handle the key stages of tech recruitment for you with our specialized team acting in the main stages of recruitment.
Talent Pool
Top-tier Brazilian tech professionals, pre-vetted and ready for action, all pre-selected and ready for new opportunities.
Our Plans
Discover the perfect plan for your needs.
Use Cases
Build my own team
Define the profile, validate with our experts, and assemble your tech team with full control over every step.
Autopilot
Put tech recruitment on autopilot and receive pre-vetted candidates matched to your needs without lifting a finger.
Complete Solution
From sourcing to hiring — an end-to-end tech recruitment experience powered by AI, specialists, and a curated talent pool.
Why Geekhunter?
Resources
HR Blog
Contents about people management, organizational culture and trends in human resources.
Glossary
A comprehensive guide to the key terms and concepts in tech recruitment and software development.
Frequently Asked Questions
Clear answers to the most common questions about using the platform and recruitment processes.
EN
Remote
(Anywhere)
Salary Range
Not informed
Experience Level
Senior
Requirements
Desired Skills
Tasks and Responsibilities
Show original1. Development of AI Models and Agents
Develop, train, fine-tune, and validate Machine Learning, Deep Learning, and generative models.
Design, implement, and optimize conversational and autonomous AI agents, including reasoning flows, memory, tools, and orchestration.
Work with NLP, computer vision, multimodal models, and RAG-based systems as needed by the business.
Perform hyperparameter tuning, fine-tuning, embeddings, and performance and cost optimization.
2. Engineering, Architecture, and Integration
Structure data pipelines for training, evaluating, and deploying models and agents.
Implement scalable solutions in cloud environments and integrate AI with APIs, databases, internal systems, web/mobile applications, and digital products.
Develop and maintain inference services, AI-driven automations, and implementations within and outside the Studio platform.
Apply MLOps practices, versioning, continuous deployment, and traceability.
3. Data, Knowledge, and Governance
Prepare, clean, and structure large volumes of structured and unstructured data.
Implement ingestion, indexing, versioning, and semantic search for knowledge bases used by agents (RAG).
Ensure quality, consistency, security, and compliance of data used in AI systems.
4. Monitoring, Evaluation, and Security
Monitor performance, latency, cost, consumption, and degradation of models and agents in production.
Define and track evaluation metrics (accuracy, precision, recall, F1-score, response quality, error rate, among others).
Implement testing, observability, auditing, output validation, and protection mechanisms against hallucinations, prompt injection, and misuse.
Promote continuous adjustments based on new data, behaviors, or business needs.
5. Innovation, Documentation, and Technical Support
Keep up with trends, frameworks, and emerging technologies in AI.
Test new approaches, tools, and applications to generate value for the business and clients.
Document architecture, flows, integrations, and usage instructions for developed solutions.
Support internal teams, facilitate knowledge transfer, and provide technical support related to AI solutions.
Propose new AI applications for the business.
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