Data Engineer (MLOps) – Fintech

Solvedex; Belo Horizonte; Teletrabalho; BRL 80.000 - 120.000; Tempo integral

Resumo da oferta

A leading fintech company is seeking a skilled Data Engineer (MLOps) to join their dynamic team. This remote role involves building robust data pipelines and ML infrastructure for payment processing systems. The ideal candidate will have experience in MLOps, Python, and AWS services, contributing to innovative financial technology projects in a fast-paced environment.

Qualificações

• 3-5 years of experience in Data Engineering focused on MLOps.
• Strong proficiency in Python and data processing frameworks.

Responsabilidades

• Design, develop, and maintain scalable data pipelines.
• Implement and optimize MLOps infrastructure on AWS.
• Collaborate with data scientists for machine learning solutions.

Conhecimentos

Python

MLOps

Problem-Solving

Ferramentas

Airflow

PySpark

AWS

SageMaker

Docker

Descrição da oferta de emprego

Location:
Remote LATAM
Industry:
Fintech & PaymentSolutions
About Solvedex
At
Solvedex , we collaboratewith
prestigious organizations
to deliver
cutting-edgefintech solutions . Our expertise lies in
custom softwaredevelopment, IT consulting, and AI-driven financial technologyservices . We are dedicated to building
scalable, high-performancepayment processing systems, fraud detection algorithms, and financialanalytics platforms .
As part of our commitment to
innovation, we are seeking a
highly skilled Data Engineer (MLOps)
tojoin a
dynamic team working on advanced financial technologyprojects . This remote role offers the opportunity to workwith
state-of-the-art machine learning and cloudinfrastructure
in a
fast-paced, growth-oriented environment.
Role Overview
We are seeking an experienced
DataEngineer with strong MLOps expertise and machine learning modelingexperience in the financial domain . In this role, you will beresponsible for building robust data pipelines and ML infrastructureto support our payment processing systems, fraud detection algorithms,and financial analytics solutions.
KeyResponsibilities
Design, develop, and maintain scalable datapipelines using
Python, Airflow, and PySpark
to process largevolumes of financial transaction data.
Implement andoptimize
MLOps infrastructure on AWS
to automate the fullmachine learning lifecycle from development to production.
Buildand maintain
deployment pipelines for MLmodels
using
SageMaker and other AWS services.
Collaborate with
data scientists and businessstakeholders
to implement machine learning solutions for
frauddetection, risk assessment, and financial forecasting.
Ensure
data quality, reliability, and security
acrossall data engineering workloads.
Optimize
dataarchitecture
to improve performance, scalability, andcost-efficiency.
Implement
monitoring and alertingsystems
to ensure production ML models perform asexpected.
Qualifications & Skills
3-5 years ofexperience
in
Data Engineering
with a focuson
MLOps
in productionenvironments.
Strong
proficiency in Pythonprogramming
and
data processing frameworks (PySpark).
Experience with
workflow orchestration tools ,particularly
Airflow .
Hands-on experience with
AWS stack, especially
SageMaker, Lambda, S3, and other relevant services.
Working knowledge of
machine learning model deployment andmonitoring
in production.
Experience with
data modelingand database systems (SQL and NoSQL) .
Knowledge of
financialservices or payment processing
domain is highlydesirable.
Familiarity with
containerization(Docker)
and
CI/CD pipelines.
Excellent
problem-solving skills
and ability to work ina
fast-paced fintech environment .

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