Machine Learning Search Engineer

Deploy•Birmingham, AL

About The Position

The Search Engineer will design and build frontend and backend systems for intelligent product search used by industrial buyers. The role covers the full lifecycle, from prototyping to deploying production services on GCP and iterating based on user data. It is an engineering role rather than a research role, with a strong foundation in Python and React and a focus on scalable retrieval pipelines, APIs, and interfaces that make AI accessible. The engineer will develop production Python services, ML inference pipelines, event-driven systems, and responsive user interfaces. They will contribute to hybrid search infrastructure, including Elasticsearch indexing, vector retrieval, relevance tuning, and search metrics. They will also help establish engineering practices for CI/CD, observability, testing, infrastructure as code, technical design, and knowledge sharing.

Requirements

At least 6 years of professional backend or full-stack engineering experience, with a strong Python and React focus.
Experience with Python and React frameworks such as Next.js, Vite.js, Remix.js, or Gatsby.js, including async 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 experience is 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.
Ability to communicate and work in cross-functional agile teams with ML engineers, architects, and product owners.
Comfort using AI tools to accelerate development.
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.
Must be eligible to work in the US 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 ML/AI patterns such as 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 mature CI/CD pipeline ownership.

Responsibilities

Design, develop, and deploy production Python services, including retrieval and ranking pipelines and client-facing APIs.
Build and integrate ML inference pipelines using 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.
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 through guidance and technical knowledge sharing.