SEO Marketing

• Design, develop, and deploy production Python services end-to-end, from retrieval and ranking pipelines through client-facing APIs
• Build and integrate ML inference pipelines, including embedding models, transformer-based classifiers, LLM-powered query understanding, and reranking services
• Develop event-driven, real-time architectures using GCP services including Cloud Run, Pub/Sub, GKE, and Cloud Functions
• Write clean, well-tested, observable Python backends and own services through deployment, monitoring, and on-call
• Drive frontend architecture decisions, establish development standards, and create reusable component libraries
• Implement hybrid retrieval systems combining keyword search, dense vector similarity, and reranking
• Build and maintain Elasticsearch indexing pipelines, query services, and relevance-tuning tooling
• Integrate vector databases into retrieval workflows
• Instrument search pipelines with CTR, zero-result rate, and latency metrics for A/B experimentation
• Build responsive, production-ready interfaces from wireframes or Figma designs
• Champion CI/CD, observability, testing, and infrastructure-as-code
• Lead design sessions with engineers and architects and translate product requirements into maintainable technical solutions
• Participate in code reviews and knowledge sharing
• Mentor mid-level and junior developers

Explore ML Search Engineer at Genuine Parts Company. Review requirements and application details.