#sejaSysMap #SysMap #soulSysMap
Senior AI Engineer
Location
Brazil
Posted
48 days ago
Salary
0
Seniority
Senior
Job Description
Senior AI Engineer
SysMap Solutions
• Design, develop, and implement production AI/ML solutions using Vertex AI and GCP. • Build data pipelines and machine learning models integrated with BigQuery. • Implement solutions using LLMs (Large Language Models), including Claude and other generative AI platforms. • Use AI-assisted coding platforms to increase productivity and code quality. • Establish and disseminate MLOps best practices, model governance, and responsible AI principles. • Collaborate with medical and business teams to translate clinical needs into technical solutions. • Ensure compliance with LGPD (Brazilian data protection law), healthcare regulations, and information security standards. • Perform code reviews and provide technical mentorship to junior and mid-level engineers. • Document architectures, processes, and technical decisions.
Job Requirements
- Google Cloud Platform (GCP): strong experience with Vertex AI for model training and deployment.
- BigQuery: advanced SQL expertise, query optimization, partitioning, and clustered tables.
- Vertex AI Workbench: notebook development and model experimentation.
- Cloud Functions, Cloud Run, or other compute services for model deployment.
- Python: advanced proficiency (key libraries: TensorFlow, PyTorch, scikit-learn, pandas).
- VS Code: experience as primary IDE, including environment setup and extensions.
- AI-assisted coding platforms: hands-on experience.
- Git/GitHub: version control, branching strategies, and code review.
- LLMs: experience implementing solutions with Claude (Anthropic), GPT, Gemini, or similar models.
- Prompt Engineering: advanced prompt construction and fine-tuning techniques.
- RAG (Retrieval-Augmented Generation): implementation of semantic search systems.
- MLOps: CI/CD for models, monitoring, versioning (MLflow, Weights & Biases, Vertex AI Experiments).
- Design patterns for production AI systems.
- Automated testing for ML models (unit tests, integration tests, model validation).
- Technical documentation and reproducibility of experiments.
- Model governance and model registry.
- Monitoring for data drift and model drift.
- Model explainability (SHAP, LIME).
- Experience with healthcare regulations (ANVISA, HIPAA, GDPR).
- Knowledge of explainable AI and model auditing.
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