Lead ML Engineer

Hi ,

Our client is looking for an Lead ML Engineer for a project and below is the detailed requirement.

• Job Title: Lead ML Engineer
• Location: Austin, TX

Qualifications & Experience:

• 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.