ML Search Engineer (Python)
Job Location:
Job Location:
Birmingham, MI - USA
Monthly Salary:
Not Disclosed
Monthly Salary:
Posted on:
8 hours ago
Posted on:
Vacancies:
1 Vacancy
Vacancies:
Job Summary
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.
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.
