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 PySpark
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
SageMaker 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 (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 and monitoring
in production.
Experience with
data modeling and database systems (SQL and NoSQL) .
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 .