ML Search Engineer

Saxon Global•Birmingham, AL

About The Position

We're building intelligent product search that understands intent, learns from behavior, and gets smarter over time. As a Senior Full Stack Engineer on the ML/AI Search team, you'll design and build both the frontend and backend systems that power product discovery for millions of industrial buyers—from scalable retrieval pipelines, APIs, and Frontend interfaces that make AI accessible. This isn't a research role. You'll own the full lifecycle: prototyping ideas, shipping production-grade services on GCP, and iterating based on real user data. Strong Python and React engineering is the foundation—if you also bring experience in search systems, vector databases, or Elasticsearch, you'll hit the ground running from day one.

Requirements

Strong Python and React foundation: 6+ years of professional backend or full-stack engineering experience with a deep Python/React (e.g., Next.js, Vite.js, Remix.js, Gatsby.js) focus—async patterns, type annotations, testing, and production-grade service/component design.
Cloud-native experience: Proven experience designing and deploying cloud-native applications (GCP strongly preferred; AWS or Azure considered).
Hands-on experience building resilient high throughput microservices and RESTful/gRPC APIs.
Solid understanding of containerization (Docker), orchestration (Kubernetes), and serverless paradigms.
Strong grounding in SOLID design principles and software craftsmanship.
Good communicator who thrives in cross-functional, agile teams alongside ML engineers, architects, and product owners.
Comfort using AI tools to accelerate development throughput.
Strong experience of using and managing Monroe
Strong understanding of relational (e.g.,PostgreSQL, MySQL, Oracle) and non-relational databases (e.g., MongoDB, DynamoDB).
Mentorship: Provide guidance and technical knowledge sharing to mid-level and junior developers.

Nice To Haves

Search systems: Experience with search platforms: Elasticsearch, OpenSearch, Solr, or Algolia—index management, query DSL, relevance tuning.
Vector search: Familiarity with vector search concepts and tooling: embeddings, approximate nearest neighbor (ANN), FAISS, Pinecone, Weaviate, or similar.
Exposure to ML/AI patterns: 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 (Terraform, Pulumi, OpenTofu) and mature CI/CD pipeline ownership.

Responsibilities

Build & Ship Search and AI-Powered Systems: 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: embedding models, transformer-based classifiers, LLM-powered query understanding, and reranking services.
Develop event-driven, real-time architectures using GCP services—Cloud Run, Pub/Sub, GKE, Cloud Functions.
Write clean, well-tested, observable Python backends; own your services through deployment, monitoring, and on-call.
Drive frontend architecture decisions, establishing development standards, and creating reusable component libraries.
Contribute to Search Infrastructure: Work alongside the Search Architect and ML Architect to 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 (Pinecone, Weaviate, FAISS or similar) into retrieval workflows—even if this is new territory, you'll learn fast.
Instrument search pipelines with meaningful metrics: CTR, zero-result rate, latency—feeding the team's A/B experimentation loop.
Build clean, responsive, and production-ready interfaces from wireframes or Figma designs.
Own the Engineering Bar: Champion CI/CD, observability, testing, and infrastructure-as-code as non-negotiables, not afterthoughts.
Lead design sessions with Engineers and Architects; translate product requirements into clean, maintainable technical solutions.
Participate in code reviews and knowledge-sharing—actively raise the team's collective skill level.