Machine Learning Engineer, Fraud Detection
Overview
Build and support production-grade fraud detection solutions for a global payments client in Dallas. Own real-time inference pipelines and graph-based fraud models that score millions of transactions daily. Partner with data science and platform teams to deploy and monitor models on Google Cloud Platform and Databricks. This role centers on low-latency APIs, feature engineering, and graph analytics with Neo4j, making immediate impact on fraud prevention.
What You'll Do10
- 1Design and ship real-time fraud detection APIs using Python and FastAPI, serving model predictions in under 100ms.
- 2Build feature engineering pipelines on Databricks that transform raw transaction streams into model-ready features.
- 3Develop graph-based fraud detection models using Neo4j to uncover complex relationships and suspicious patterns.
- 4Deploy machine learning models to Google Cloud Platform using Vertex AI and Cloud Run, ensuring production readiness.
- 5Monitor model drift and data quality in production, implementing retraining workflows as needed.
- 6Collaborate with data scientists to translate offline experiments into scalable, real-time inference systems.
- 7Optimize API and database performance to handle peak transaction volumes without latency spikes.
- 8Document system architecture and maintain runbooks for incident response.
- 9Support production deployments during launch windows, providing on-call expertise.
- 10Drive the adoption of best practices in ML engineering, including CI/CD and automated testing.
Requirements8
- 15+ years in machine learning engineering or related field, with a focus on fraud detection.
- 2Strong proficiency in Python and experience building RESTful APIs with FastAPI or Flask.
- 3Hands-on experience with Google Cloud Platform services: BigQuery, Vertex AI, Cloud Run.
- 4Working knowledge of Databricks for data processing and ML workflows.
- 5Experience with graph databases, particularly Neo4j, and graph algorithms.
- 6Proven track record of deploying and monitoring models in production.
- 7Understanding of real-time inference constraints and low-latency architectural patterns.
- 8Excellent problem-solving and communication skills, with a collaborative mindset.
Salary Insight
Salary not disclosed in listing
Similar open positions
Explore active roles that match your skills and interests.
Compugra Systems
VerifiedMachine Learning Engineer Fraud Detection
You will own production-grade fraud detection models for a Dallas-based fintech, scaling to 8-12 years of expertise. Join a team of 5 engineers and data scientists, partnering with risk and platform teams. This contract role stands out for its focus on real-time inference and graph analytics, requiring deep GCP and Databricks proficiency.
SmallArc, Inc
VerifiedLead Data Scientist, Fraud Analytics & ML
Own the fraud detection strategy for a financial services client, reducing fraud losses by 20% within the first year. Lead a team of 5 data scientists and collaborate with engineering to deploy ML models in production. You will work directly with the VP of Risk to align fraud use cases with business goals. This role stands out for its focus on Graph Analytics and AI techniques to uncover hidden fraud rings.
Lorven Technologies, Inc.
VerifiedLead MLOps Engineer, Fraud Detection & GCP
You will lead the design and deployment of enterprise-scale fraud detection platforms, owning the ML lifecycle from model training to production. Your stack centers on Google Cloud Platform and Agentic AI Architecture, with a team of data scientists and platform engineers. You will set the MLOps roadmap and drive automation, making this role pivotal for fraud prevention at scale.
Compugra Systems
VerifiedLead MLOps Engineer Fraud Detection Platform
Own end to end ML deployment frameworks for enterprise scale fraud detection systems at Compugra Systems in Dallas Texas. Drive scalability reliability and governance while leading cross functional teams.
AgreeYa Solutions
VerifiedML Engineer, Databricks & Python
You will own the machine learning engineering lifecycle for a Dallas-based client, designing and deploying scalable data pipelines. You will work with Python, PySpark, and Databricks to process streaming and batch data, collaborating with data scientists and software engineers. You will build real-time features using Kafka and Snowflake, ensuring low-latency model serving. This 18-month contract offers the chance to tackle complex data modeling challenges, including slowly changing dimensions in MongoDB and PostgreSQL.
Midasis Technologies LLC
VerifiedSenior Software Engineer, Risk & Fraud Detection
Senior Software Engineer owning the architecture of backend services and APIs for the Risk Team, supporting fraud detection, risk scoring, and compliance automation at Midasis Technologies LLC. You will build scalable, secure, and resilient systems that handle transaction monitoring and compliance workflows in a high-stakes environment. Collaborating closely with InfoSec, Data Science, and Product teams, you will drive technical direction and ensure system integrity. This onsite role in Los Angeles offers the chance to shape mission-critical systems from day one.