Senior AI Engineer

Veraxion; São Paulo; Presencial; BRL 120.000 - 160.000; Tempo integral

Resumo da oferta

A leading tech firm in São Paulo is seeking a Senior AI Engineer to drive AI initiatives from problem framing to model deployment. The ideal candidate has over 10 years of experience, with a strong background in data science and machine learning. Responsibilities include building models, defining evaluation frameworks, and mentoring junior staff. Candidates should have expertise in Python, SQL, and ML libraries and be comfortable in client-facing roles. This is a great opportunity to impact AI-driven solutions in the industry.

Qualificações

• 10+ years of total experience, with 5+ years in applied data science or machine learning.
• Strong foundation in statistics, optimization, and data storytelling.
• Ability to translate domain-specific challenges into measurable data science outcomes.

Responsabilidades

• Lead end-to-end AI driven initiatives — from problem framing and data exploration to model deployment.
• Build and optimize predictive, prescriptive, and generative models using modern ML techniques.
• Define and implement model evaluation frameworks — accuracy, drift detection, explainability, and impact metrics.

Conhecimentos

Python

SQL

Machine Learning libraries

Statistics

Data storytelling

MLOps frameworks

Communication

Mentoring

Ferramentas

scikit-learn

XGBoost

PyTorch

TensorFlow

AWS

Azure

GCP

SageMaker

MLflow

Kubeflow

Descrição da oferta de emprego

We are looking for a Senior AI Engineer who can translate business problems into scalable analytical solutions and lead the design of AI-driven solutions. You will work at the intersection of data, domain, and decision-making, collaborating with engineering and strategy teams to operationalize machine learning in real-world contexts.

Key Responsibilities

Lead end-to-end AI driven initiatives — from problem framing and data exploration to model deployment.

Build and optimize predictive, prescriptive, and generative models using modern ML techniques.

Partner with data engineering teams to ensure robust data pipelines and governance for model reliability.

Define and implement model evaluation frameworks — accuracy, drift detection, explainability, and impact metrics.

Mentor junior data scientists and analysts; establish coding and experimentation best practices.

Collaborate with business stakeholders to identify high-value AI use cases and prototype proofs of concept.

Required Skills & Experience

10+ years of total experience, with 5+ years in applied data science or machine learning.

Expertise in Python, SQL, and ML libraries (scikit-learn, XGBoost, PyTorch, TensorFlow).

Strong foundation in statistics, optimization, and data storytelling.

Experience with cloud environments (AWS, Azure, or GCP) and MLOps frameworks (SageMaker, MLflow, Kubeflow).

Exposure to LLMs, NLP, or Generative AI for applied use cases.

Ability to translate domain-specific challenges into measurable data science outcomes — ideally in energy, commodities, or financial analytics.

Excellent communication and mentoring abilities; comfortable working in client-facing roles.

Nice to Have

Experience integrating data science into data platforms or governance frameworks.

Prior experience in consulting or product environments.