> Markdown version of [/jobs/ext/455563-ml-search-engineer-python](https://www.wearedevelopers.com/jobs/ext/455563-ml-search-engineer-python). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # ML Search Engineer (Python) - **Company:** STM Consulting, Inc. - **Location:** Birmingham, AL, United States - **Experience:** Expert - **Salary:** $85,280.0 - $97,760.0 - **Contract:** Temporary to permanent - **Skills:** A/B Testing, Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Automated Storage and Retrieval Systems, User Authentication, Microsoft Azure, Cloud Computing, Code Review, Continuous Integration, Elasticsearch, Python (Programming Language), Search Technologies, Apache Solr, Pulumi, Google Cloud, Spring Cloud, Large Language Models, Multi-Agent Systems, Prompt Engineering, Backend, Customer Identity Access Management, Solid Principles, Kubernetes, Infrastructure Automation Frameworks, Terraform, Grpc, Automation Anywhere, Serverless Computing, Docker, Microservices - **Published:** June 4, 2026 - **Apply:** https://www.careerjet.com/jobad/us60452be64d76e5a0d586c2e94d4c5072 ## About the Role 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. ## Description 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… + 5 days ago, Kforce's client in Birmingham, AL is seeking a Customer Authentication Engineer III to join the Authentication Core Team supporting digital authentication, identity orchestration, … + 6 days ago ## Related Videos - [Harry Potter and the Elastic Semantic Search](https://www.wearedevelopers.com/videos/860-harry-potter-and-the-elastic-semantic-search) - [Why segmenting your infrastructure into tiers makes your infrastructure design better](https://www.wearedevelopers.com/videos/1960-why-segmenting-your-infrastructure-into-tiers-makes-your-infrastructure-design-better) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [Exploring the Power of gRPC-Gateway for Writing RESTful Services](https://www.wearedevelopers.com/videos/2072-exploring-the-power-of-grpc-gateway-for-writing-restful-services) - [Unleashing Potential Across Teams: The Power of Infrastructure as Code](https://www.wearedevelopers.com/videos/930-unleashing-potential-across-teams-the-power-of-infrastructure-as-code) - [Docker build without Docker](https://www.wearedevelopers.com/videos/100114-docker-build-without-docker) ## Related Articles - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [SEO in an AI world - Google vs. ChatGPT and survival tips for content creators](https://www.wearedevelopers.com/magazine/534-seo-in-an-ai-world-google-vs-chatgpt-and-survival-tips-for-content-creators) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Got AI ideas but no money? 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