ML Search Engineer (Python)

Job Type   /   Job Level

Full-time   /   Others/Any

Job Location

Birmingham, AL

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Job Summary (List Format): Senior Python Engineer, ML/AI Search Team

Core Responsibilities

Design, develop, and deploy end-to-end Python backend services for intelligent product search.
Integrate and build ML inference pipelines using embeddings, transformer models, and LLMs for query understanding and reranking.
Develop scalable retrieval systems, real-time architectures, and customer-facing APIs on Google Cloud Platform (GCP).
Own production services including testing, monitoring, observability, and on-call support.
Collaborate with Search and ML Architects to create hybrid retrieval systems (keyword, vector similarity, ML reranking).
Maintain Elasticsearch indexing pipelines and integrate vector databases (e.g., Pinecone, FAISS) into retrieval workflows.
Instrument systems with metrics (CTR, zero result rate, latency) to support A/B testing and experimentation.
Champion engineering best practices: CI/CD, infrastructure as code, testing, and observability.
Lead technical design discussions and participate in code reviews and team knowledge sharing.

Requirements

4+ years professional backend or full stack engineering experience, with a strong focus on Python.
Experience building and deploying cloud-native applications (preferably on GCP; AWS/Azure also welcome).
Strong skills in microservices, REST/GRPC APIs, Docker, Kubernetes, and serverless patterns.
Solid understanding of software design principles and best engineering practices.
Excellent communication; comfortable collaborating with ML engineers, architects, and product teams.
Willingness to utilize AI tools to accelerate development.

Preferred Qualifications

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

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