Full time Python ML Search Engine

Saxon Global•Austin, TX

5d•Remote

About The Position

We're building the next generation of intelligent product search—systems that understand user intent, learn from behavior, and continuously improve. As a Senior Python Engineer on our ML/AI Search team, you'll architect and build the backend services that power product discovery for millions of industrial buyers. This is a hands-on engineering role focused on prototyping, shipping, and iterating quickly, deploying production systems on GCP and improving them based on real-world performance. If you love Python, distributed systems, and search technology, you'll thrive here.

Requirements

4+ years of professional backend or full-stack engineering experience with a strong Python focus.
Experience building and deploying cloud-native applications (GCP preferred; AWS/Azure welcome).
Hands-on experience with microservices, REST/gRPC APIs, Docker, Kubernetes, and serverless patterns.
Strong grounding in software design principles and clean engineering practices.
Excellent communication skills and comfort working with ML engineers, architects, and product teams.
Willingness to use AI tools to accelerate development.

Nice To Haves

Experience with search platforms (Elasticsearch, OpenSearch, Solr, Algolia).
Familiarity with vector search concepts and tools (embeddings, ANN, FAISS, Pinecone, Weaviate).
Exposure to ML/AI workflows: RAG pipelines, LLM integration, prompt engineering, fine-tuning.
Experience with AI orchestration frameworks (LangChain, LangGraph, Google ADK).
Infrastructure-as-code (Terraform, Pulumi) and mature CI/CD pipeline ownership.

Responsibilities

Design, develop, and deploy end-to-end Python services—from retrieval and ranking pipelines to customer-facing APIs.
Integrate ML inference pipelines: embeddings, transformer models, LLM-powered query understanding, and reranking.
Build event-driven, real-time architectures using GCP (Cloud Run, Pub/Sub, GKE, Cloud Functions).
Own your services in production: testing, observability, monitoring, and on-call.
Partner with Search and ML Architects to build hybrid retrieval systems combining keyword search, vector similarity, and ML reranking.
Maintain Elasticsearch indexing pipelines, query services, and relevance-tuning tools.
Integrate vector databases (Pinecone, Weaviate, FAISS, etc.) into retrieval workflows.
Instrument pipelines with meaningful metrics (CTR, zero-result rate, latency) to drive A/B experimentation.
Champion CI/CD, observability, testing, and infrastructure-as-code.
Lead design discussions and translate product requirements into clean, scalable solutions.
Participate in code reviews and knowledge-sharing to elevate the entire team.