Salesforce CPQ Architect & Machine Learning Engineer - Fraud Detection

Position: Salesforce CPQ Architect

Location: Milwaukee, WI (Onsite)

Summary:

• A hands-on Salesforce CPQ Architect to lead the design and delivery of complex Quote-to-Cash solutions for an enterprise client.
• You will own the end-to-end CPQ architecture - from product catalog and pricing strategy through quoting, approvals, contracts, and downstream billing/ERP integration - while working onsite alongside client business and IT stakeholders.

Description:

• Bachelor's degree or Master's Degree in Computer science, or a related field, with minimum 12+ Years of relevant experience.
• 5+ years of hands-on Salesforce CPQ implementation experience, with 2+ full-lifecycle enterprise deployments in an architect role
• Deep expertise in product bundling, price rules, quote calculator plugins (QCP), advanced approvals, and contract/amendment/renewal flows
• Strong understanding of Quote-to-Cash and Lead-to-Order processes, including billing and revenue recognition touchpoints
• Experience integrating Salesforce with ERP systems (SAP, Oracle, NetSuite) via middleware
• Proficiency with Sales Cloud architecture, Flow, Apex (read/review level), and data modeling
• Salesforce CPQ Specialist certification required; Application Architect or System Architect strongly preferred
• Excellent client-facing communication skills and proven ability to operate independently onsite
• Exposure to Salesforce Revenue Cloud / Revenue Lifecycle Management (RLM) and migration considerations from legacy CPQ
• Experience with Conga or DocuSign CLM, Salesforce Billing, or subscription/recurring-revenue models (e.g., monitored services, SaaS-attached hardware)
• Industry experience in building technologies, HVAC, fire & security, industrial equipment, or discrete manufacturing - including engineered-to-order and configure-to-order product models
• Familiarity with channel/dealer quoting models and field sales organizations
• Prior consulting background (Big 4 or boutique SI)

Position: Machine Learning Engineer - Fraud Detection

Location: Dallas, TX (Hybrid)

Description:

• Bachelor's degree or Master's Degree in Computer science, or a related field, with minimum 12+ Years of relevant experience.

Role Summary

• We are looking for a Machine Learning Engineer to build and support production-grade fraud detection solutions.
• The role focuses on real-time inference, feature engineering, APIs, graph-based fraud detection, and production deployment support.

Key Skills

• Good Experience in Machine Learning Engineering and Real-Time Inference
• Strong in Python, APIs, and Microservices
• Good Experience in GCP and Databricks
• Strong in Neo4j / Graph Databases and Feature Stores
• Data Pipelines and Feature Engineering
• MLOps, Monitoring, and Production Support
• Agentic AI Architecture (good to have)

Required Qualifications

• Hands-on experience in Python and ML model deployment.
• Experience with APIs, microservices, and production ML systems .
• Knowledge of data pipelines, data engineering, and feature stores.
• Exposure to GCP, Databricks, Data Lake, or Data Warehouse platforms.
• Basic understanding of MLOps, monitoring, and release support .
• Good communication and problem-solving skills.

Nice to Have

• Fraud detection, risk analytics, or scoring model experience.
• Experience with Neo4j or graph-based ML solutions.
• Understanding of Agentic AI architecture.