ML Search Engineer (Python)

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., Job Title: Cloud Infrastructure engineer Location: Birmingham, AL or Remote (1 week travel initially during onboarding) Duration: 6 Months approx. and can be extended PURPOSE…

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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.

• $41.00-47.00 per hour