Data Engineer (MLOps) – Fintech

Solvedex; Jaboatão dos Guararapes; Teletrabalho; BRL 60.000 - 100.000; Tempo integral

Resumo da oferta

An innovative fintech company is on the lookout for a talented Data Engineer with expertise in MLOps and machine learning. This remote position allows you to leverage cutting-edge technologies to build scalable data pipelines and optimize machine learning models in a dynamic environment. Collaborate with a team of experts to enhance financial analytics and fraud detection systems, ensuring data quality and security. If you're passionate about transforming the financial landscape through technology, this opportunity offers the perfect platform for your skills and ambitions.

Qualificações

• 3-5 years of experience in Data Engineering with MLOps.
• Proficiency in Python and data processing frameworks like PySpark.

Responsabilidades

• Design and maintain scalable data pipelines using Python and Airflow.
• Implement MLOps infrastructure on AWS for the machine learning lifecycle.

Conhecimentos

Data Engineering

MLOps

Python

PySpark

Airflow

AWS

SageMaker

SQL

NoSQL

Docker

Ferramentas

AWS Lambda

AWS S3

CI/CD pipelines

Descrição da oferta de emprego

Below is the refined version of the provided job description, with improved formatting and focus on relevant content:

About Solvedex

At Solvedex, we collaborate with prestigious organizations to deliver cutting-edge fintech solutions. Our expertise includes custom software development, IT consulting, and AI-driven financial technology services. We are dedicated to building scalable, high-performance payment processing systems, fraud detection algorithms, and financial analytics platforms.

Position Overview

We are seeking a highly skilled Data Engineer with strong MLOps expertise and machine learning modeling experience in the financial domain. This remote role offers the opportunity to work with state-of-the-art machine learning and cloud infrastructure in a fast-paced, growth-oriented environment.

Responsibilities

1. Design, develop, and maintain scalable data pipelines using Python, Airflow, and PySpark for processing large volumes of financial transaction data.
2. Implement and optimize MLOps infrastructure on AWS to automate the full machine learning lifecycle.
3. Build and maintain deployment pipelines for ML models using SageMaker and other AWS services.
4. Collaborate with data scientists and stakeholders to implement solutions for fraud detection, risk assessment, and financial forecasting.
5. Ensure data quality, reliability, and security across all workloads.
6. Optimize data architecture for performance, scalability, and cost-efficiency.
7. Implement monitoring and alerting systems for production ML models.

Qualifications & Skills

• 3-5 years of experience in Data Engineering with a focus on MLOps in production environments.
• Proficiency in Python and data processing frameworks like PySpark.
• Experience with workflow orchestration tools, especially Airflow.
• Hands-on experience with AWS services such as SageMaker, Lambda, and S3.
• Knowledge of machine learning model deployment and monitoring.
• Experience with SQL and NoSQL databases.
• Familiarity with containerization (Docker) and CI/CD pipelines.
• Understanding of the financial services or payment processing domain is highly desirable.
• Excellent problem-solving skills and ability to work in a fast-paced environment.

Additional Information

Location: Remote LATAM

Industry: Fintech & Payment Solutions