Pessoa Engenheira de Machine Learning Sênior

Jobgether · Brazil

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Pessoa Engenheira de Machine Learning Sênior based in Brazil.

This is a high-impact, senior-level role focused on bridging machine learning research, software engineering, and large-scale production systems. You will work across the full ML lifecycle, from experimentation and dataset curation to deployment, monitoring, and continuous optimization of models in production. The role involves building and maintaining robust MLOps and LLMOps practices to ensure scalability, reliability, and performance of AI-driven solutions. You will work with advanced components such as embeddings, ranking systems, and language models, directly influencing how AI is applied in real-world products. The environment is highly technical and collaborative, involving close interaction with data, platform, and engineering teams. This position is ideal for someone who thrives on turning experimental models into production-grade systems that deliver measurable business value.

Accountabilities:

Design, develop, evaluate, and operationalize machine learning models and AI components for production environments.
Build and maintain pipelines for training, experimentation, validation, versioning, deployment, and monitoring of ML models.
Work with embeddings, rerankers, NLP models, classification systems, and retrieval-based architectures.
Implement and evolve MLOps and LLMOps practices to ensure scalable and reliable model lifecycle management.
Monitor model performance across dimensions such as accuracy, latency, cost, drift, and stability.
Support strategies for fine-tuning, prompt optimization, dataset curation, fallback mechanisms, and inference optimization.
Define and implement evaluation frameworks, metrics, and testing strategies for production-grade AI systems.
Collaborate with engineering, data, and platform teams to integrate ML models into APIs, services, and applications.
Document experiments, technical decisions, and reusable standards for model development and deployment.

Requirements:

Strong experience in applied Machine Learning, model evaluation, and production deployment.
Proficiency in Python and ML frameworks such as scikit-learn, PyTorch, TensorFlow, Hugging Face, or equivalents.
Solid understanding of NLP, embeddings, information retrieval, classification, and model evaluation techniques.
Experience building ML pipelines, managing datasets, and versioning models and experiments.
Strong knowledge of ML concepts such as overfitting, drift, bias, validation, and performance metrics.
Experience deploying models using cloud platforms, APIs, containers, and modern infrastructure practices.
Familiarity with MLOps tools and workflows for scalable model operations.
Awareness of security, privacy, governance, and traceability in AI systems.

Differentials:

Experience with LLMOps practices such as RAG evaluation, LLM-as-a-judge, and AI observability.
Knowledge of model optimization techniques such as fine-tuning, LoRA, quantization, and inference optimization.
Experience with tools like MLflow, Kubeflow, BentoML, Ray, vLLM, Triton, or cloud ML platforms (AWS, GCP, Azure ML, Vertex AI, SageMaker).
Familiarity with vector databases, hybrid search, reranking systems, and retrieval-augmented pipelines.
Experience working with open-source and proprietary LLM providers and model comparison strategies.
Experience in regulated environments requiring explainability, auditing, and model governance.

Soft Skills:

Strong analytical thinking and problem-solving mindset.
Proactive, experimental, and pragmatic approach to engineering challenges.
Strong communication skills with both technical and non-technical stakeholders.
Ability to work collaboratively in agile, cross-functional teams.
Structured thinking, curiosity, and continuous improvement mindset.
Ability to balance experimentation with production reliability.

Benefits:

Cooperative contract model.
Fully remote work model (Anywhere Office).
BYOD support for device acquisition and usage.
Home office allowance via flexible benefits card.
Wellness package including TotalPass and health platforms (WellHub, Avus, Starbem, Dasa+ Saúde).
Additional benefits such as temporary incapacity daily allowance, benefits club, and birthday day off.
Training programs and certification support for professional development.

Data & ML pay context

Based on 1,466 disclosed Data & ML salaries on RoleSuite, the role pays a median of $161K/year, with most offers between $127K and $203K (10th–90th percentile: $102K–$244K).

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Pessoa Engenheira de Machine Learning Sênior

Accountabilities:

Requirements:

Benefits:

Data & ML pay context

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