ML Search Engineer

Genuine Parts Company•Birmingham, AL

About The Position

As a Senior Full Stack Engineer on the ML/AI Search team, you will design and build frontend and backend systems that power product discovery for industrial buyers. The role spans scalable retrieval pipelines, APIs, and interfaces that make AI accessible. It is an engineering and delivery role rather than a research role: you will prototype ideas, ship production-grade services on GCP, and iterate using real user data. You will build Python services and ML inference pipelines, develop event-driven architectures, and contribute to search infrastructure such as hybrid retrieval, Elasticsearch indexing, vector database integrations, and relevance measurement. You will also help set frontend architecture and engineering standards, build reusable components and responsive interfaces, and promote CI/CD, observability, testing, and infrastructure-as-code. The role includes collaboration with engineers, architects, ML specialists, and product owners, as well as mentoring junior and mid-level developers.

Requirements

At least 6 years of professional backend or full-stack engineering experience, with a strong Python and React focus.
Experience with React frameworks such as Next.js, Vite.js, Remix.js, or Gatsby.js, and with asynchronous patterns, type annotations, testing, and production-grade service or component design.
Experience designing and deploying cloud-native applications; GCP is strongly preferred, while AWS or Azure is also considered.
Hands-on experience building resilient, high-throughput microservices and RESTful or gRPC APIs.
Understanding of Docker, Kubernetes, and serverless paradigms.
Strong grounding in SOLID design principles and software craftsmanship.
Strong experience using and managing monorepos.
Understanding of relational databases such as PostgreSQL, MySQL, or Oracle and non-relational databases such as MongoDB or DynamoDB.
Ability to communicate and work in cross-functional agile teams with ML engineers, architects, and product owners.
Comfort using AI tools to accelerate development.
Ability to mentor mid-level and junior developers.
Must be eligible to work in the United States without visa sponsorship.

Nice To Haves

Experience with search platforms such as Elasticsearch, OpenSearch, Solr, or Algolia, including index management, query DSL, and relevance tuning.
Familiarity with vector search concepts and tools such as embeddings, approximate nearest neighbor (ANN), FAISS, Pinecone, or Weaviate.
Exposure to RAG pipelines, LLM integration, prompt engineering, or fine-tuning workflows.
Experience with AI orchestration frameworks such as LangChain, LangGraph, or Google ADK.
Infrastructure-as-code experience with Terraform, Pulumi, or OpenTofu, and ownership of mature CI/CD pipelines.

Responsibilities

Design, develop, and deploy production Python services, including retrieval and ranking pipelines and client-facing APIs.
Build and integrate ML inference pipelines involving embedding models, transformer-based classifiers, LLM-powered query understanding, and reranking services.
Develop event-driven, real-time architectures using GCP services such as Cloud Run, Pub/Sub, GKE, and Cloud Functions.
Write tested and observable Python backends and own services through deployment, monitoring, and on-call.
Drive frontend architecture decisions, establish development standards, and create reusable component libraries.
Work with the Search Architect and ML Architect to implement hybrid retrieval combining keyword search, dense vector similarity, and reranking.
Build and maintain Elasticsearch indexing pipelines, query services, and relevance-tuning tools.
Integrate vector databases into retrieval workflows.
Instrument search pipelines with metrics such as CTR, zero-result rate, and latency to support A/B experimentation.
Build responsive, production-ready interfaces from wireframes or Figma designs.
Promote 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 and provide technical guidance to mid-level and junior developers.