Lead MLOps Engineer - Fraud Detection Platform

Lorven Technologies Inc

Austin, TX, United States

14 days ago

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Role details

Contract type

Permanent contract

Employment type

Full-time (> 32 hours)

Experience level

Expert

Compensation

$110,000.0 - $140,000.0

Working hours

Regular working hours

Job source

www.careerjet.com

Tech stack

Artificial Intelligence Cloud Computing Data Cleansing Data Warehousing Fraud Prevention and Detection Graph Database Python (Programming Language) Machine Learning Neo4j Release Management Tensorflow Software Engineering

+11 more

Google Cloud Feature Engineering Data Lakes Information Technology Data Management Machine Learning Operations Virtual Agents Restful APIs Data Pipelines Databricks Microservices

Requirements

Bachelor’s degree in Computer Science, Information Technology, Engineering, or a related field.
7 12 years of experience in Machine Learning Engineering, AI/ML development, or a related discipline.
Strong experience building, developing, deploying, and supporting production-grade Machine Learning models, preferably for fraud detection, risk analytics, or scoring solutions.
Hands-on expertise in Python and ML frameworks, with experience developing and deploying production ML models.
Strong experience with real-time ML inference and designing low-latency solutions with a target response time of less than 250 ms.
Hands-on experience developing REST APIs, microservices, and ML-powered services for production applications.
Strong experience with feature engineering, feature preparation, data pipelines, and feature stores for ML use cases.
Experience with GCP, Databricks, Data Lakes, and/or Data Warehouse platforms in cloud-based ML environments.
Experience with Neo4j, graph databases, or graph-based ML solutions, preferably in fraud detection or relationship-based analytics.
Strong understanding of MLOps, including model deployment, release management, monitoring, performance tracking, production support, and lifecycle management.
Experience improving model scoring performance, reliability, scalability, and operational efficiency in production environments.
Strong understanding of end-to-end ML workflows, including data preparation, feature engineering, model development, deployment, inference, monitoring, and maintenance.
Experience supporting data quality, governance, operational activities, and production troubleshooting for ML/data platforms.
Familiarity with fraud detection, risk analytics, fraud scoring models, or financial crime use cases is highly preferred.
Understanding of Agentic AI architecture is a plus.
Strong communication, analytical, troubleshooting, and problem-solving skills with the ability to collaborate effectively with Data Scientists, ML Engineers, Data Engineers, MLOps, and application development teams.