Location:

Remote LATAM
Industry:

Fintech & Payment Solutions

About Solvedex
At

Solvedex , we collaborate with

prestigious organizations

to deliver

cutting-edge fintech solutions. Our expertise lies in

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.
As part of our commitment to

innovation , we are seeking a

highly skilled Data Engineer (MLOps)

to join a

dynamic team working on advanced financial technology projects. 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.

Role Overview
We are seeking an experienced

Data Engineer with strong MLOps expertise and machine learning modeling experience in the financial domain. In this role, you will be responsible for building robust data pipelines and ML infrastructure to support our payment processing systems, fraud detection algorithms, and financial analytics solutions.

Key Responsibilities
Design, develop, and maintain scalable data pipelines using

Python, Airflow, and Py Spark

to process large volumes of financial transaction data.
Implement and optimize

MLOps infrastructure on AWS

to automate the full machine learning lifecycle from development to production.
Build and maintain

deployment pipelines for ML models

using

Sage Maker and other AWS services.
Collaborate with

data scientists and business stakeholders

to implement machine learning solutions for

fraud detection, risk assessment, and financial forecasting.
Ensure

data quality, reliability, and security

across all data engineering workloads.
Optimize

data architecture

to improve performance, scalability, and cost-efficiency.
Implement

monitoring and alerting systems

to ensure production ML models perform as expected.

Qualifications & Skills
3-5 years of experience

in

Data Engineering

with a focus on

MLOps

in production environments.
Strong

proficiency in Python programming

and

data processing frameworks (Py Spark).
Experience with

workflow orchestration tools , particularly

Airflow.
Hands-on experience with

AWS stack , especially

Sage Maker, Lambda, S3, and other relevant services.
Working knowledge of

machine learning model deployment and monitoring

in production.
Experience with

data modeling and database systems (SQL and No SQL).
Knowledge of

financial services or payment processing

domain is highly desirable.
Familiarity with

containerization (Docker)

and

CI/CD pipelines.
Excellent

problem-solving skills

and ability to work in a

fast-paced fintech environment.