BravoTech
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Lead Software Engineer – AI/ML & Dynamic Pricing
Location: Onsite – Grapevine, TX (5 days per week)
As part of our continued investment in advanced technology and intelligent commerce, we are seeking a Lead Software Engineer – AI/ML & Dynamic Pricing to architect and deliver real-time pricing intelligence systems that directly impact revenue, margin optimization, and customer experience.
This role sits at the intersection of machine learning, high-scale backend systems, and real-time pricing execution. You will lead the design and implementation of AI/ML-driven dynamic pricing models that ingest multiple live pricing signals (market data, competitive data, historical sales, demand elasticity, inventory position, promotions, etc.) and translate them into revenue-impacting pricing decisions across our eCommerce and retail ecosystem.
You will partner closely with engineering leadership, data science, product, and platform teams to build intelligent systems that are resilient, explainable, and optimized for real-time execution.
What You’ll Do
AI/ML & Pricing Architecture
Core Experience
Why This Role Is High Impact
This role owns systems that make high-stakes revenue-impacting decisions in real time. The right candidate understands not only how to build ML models, but how to operationalize them inside robust backend systems where availability, accuracy, and performance directly affect financial outcomes.
IND123
Lead Software Engineer (AI/ML Dynamic Pricing)
2026-01-23
2026-03-14
Category: Application Development
Job Number: 44135
Location Requirements: On-Site
Job Description
Lead Software Engineer – AI/ML & Dynamic Pricing
Location: Onsite – Grapevine, TX (5 days per week)
As part of our continued investment in advanced technology and intelligent commerce, we are seeking a Lead Software Engineer – AI/ML & Dynamic Pricing to architect and deliver real-time pricing intelligence systems that directly impact revenue, margin optimization, and customer experience.
This role sits at the intersection of machine learning, high-scale backend systems, and real-time pricing execution. You will lead the design and implementation of AI/ML-driven dynamic pricing models that ingest multiple live pricing signals (market data, competitive data, historical sales, demand elasticity, inventory position, promotions, etc.) and translate them into revenue-impacting pricing decisions across our eCommerce and retail ecosystem.
You will partner closely with engineering leadership, data science, product, and platform teams to build intelligent systems that are resilient, explainable, and optimized for real-time execution.
What You’ll Do
AI/ML & Pricing Architecture
- Lead the architecture and implementation of AI/ML models that power real-time dynamic pricing decisions.
- Design intelligent pricing systems that combine predictive modeling, elasticity modeling, demand forecasting, and optimization algorithms.
- Translate ML model outputs into production-grade pricing engine decisions with strict latency requirements.
- Ensure model explainability, monitoring, and continuous improvement in live production environments.
- Own and evolve a dynamic pricing engine that integrates multiple real-time pricing sources (competitive data, web crawls, market feeds, transactional data).
- Architect systems capable of ingesting high-volume external and internal data streams with low-latency processing.
- Design safeguards and validation mechanisms for high-revenue-impact pricing decisions.
- Balance margin optimization, competitive positioning, and customer experience in pricing logic.
- Design and implement high-throughput data pipelines for structured and unstructured pricing inputs.
- Build distributed systems capable of handling real-time web scraping, event streams, and transactional updates.
- Optimize system performance to meet strict SLA and availability requirements.
- Help shape a modern technology stack (AWS, Kubernetes, service-oriented architecture, CI/CD, observability frameworks).
- Establish engineering best practices around testing, deployment, monitoring, and model lifecycle management.
- Mentor engineers in backend architecture, ML system integration, and scalable design patterns.
- Serve as a technical owner for large-scale, revenue-critical systems.
Core Experience
- 9+ years of software engineering experience with backend, distributed systems.
- Hands-on experience building or owning a dynamic pricing engine with multiple real-time price sources.
- Experience deploying AI/ML models into production systems that require low-latency decisioning.
- Experience integrating ML outputs into transactional systems (payments, rewards, or eCommerce workflows).
- Experience working with real-time web scraping and external market data ingestion.
- Practical experience with predictive modeling, demand forecasting, pricing optimization, or elasticity modeling.
- Experience operationalizing ML models (model versioning, monitoring, drift detection, retraining workflows).
- Strong understanding of data pipelines, streaming architectures, and distributed processing.
- Deep expertise in Java/Spring for backend system development.
- Strong AWS experience (compute, storage, networking, managed services).
- Kubernetes and containerized microservices architecture experience.
- CI/CD pipeline implementation and DevOps practices.
- Experience building highly available, high-throughput systems.
- Strong eCommerce and/or retail technology background.
- Experience working on systems that directly impact revenue, margin, or pricing strategy.
- Production ML-driven pricing models influencing real-time revenue decisions.
- A resilient, scalable pricing engine capable of processing multiple real-time pricing signals.
- Reduced latency between data ingestion and pricing decision.
- Increased margin optimization and competitive responsiveness.
- Engineering best practices established across AI-integrated backend systems.
Why This Role Is High Impact
This role owns systems that make high-stakes revenue-impacting decisions in real time. The right candidate understands not only how to build ML models, but how to operationalize them inside robust backend systems where availability, accuracy, and performance directly affect financial outcomes.
IND123
Meet Your Recruiter
Andrea Lee
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