ML Search Engineer

Genuine Parts Company

. Design, develop, and deploy production Python services end-to-end, from retrieval and ranking pipelines through client-facing APIs .

Posted 11/9/2026full-timeBirmingham • Alabama • United StatesMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in designing and deploying cloud-native applications using Python and React, with a strong focus on building resilient microservices and integrating machine learning inference pipelines. Proficient in implementing CI/CD practices and observability in production environments.

Highest-signal resume keywords

Python DevelopmentGCP ServicesMicroservices ArchitectureElasticsearchCI/CD Implementation

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

PythonReactMicroservicesRESTful APIsGCPElasticsearchDockerKubernetesSQL DatabasesNoSQL Databases

Soft Skills

MentoringCollaborationCode ReviewAgile Methodologies

Tools & Technologies

Cloud RunPub/SubGKECloud FunctionsTerraformLangChainLangGraph

Industry Keywords

Cloud-Native ApplicationsEvent-Driven ArchitectureMachine LearningSearch PlatformsVector Search

Tech Stack

Tools & technologies

AWSAzureCloudDockerDynamoDBElasticSearchGoogle Cloud PlatformGRPCKubernetesMicroservicesMongoDBMySQLOraclePostgresPythonReactTerraform

About the role

Key responsibilities & impact

Design, develop, and deploy production Python services end-to-end, from retrieval and ranking pipelines through client-facing APIs
Build and integrate ML inference pipelines, including embedding models, transformer-based classifiers, LLM-powered query understanding, and reranking services
Develop event-driven, real-time architectures using GCP services including Cloud Run, Pub/Sub, GKE, and Cloud Functions
Write clean, well-tested, observable Python backends and own services through deployment, monitoring, and on-call
Drive frontend architecture decisions, establish development standards, and create reusable component libraries
Implement hybrid retrieval systems combining keyword search, dense vector similarity, and reranking
Build and maintain Elasticsearch indexing pipelines, query services, and relevance-tuning tooling
Integrate vector databases into retrieval workflows
Instrument search pipelines with CTR, zero-result rate, and latency metrics for A/B experimentation
Build responsive, production-ready interfaces from wireframes or Figma designs
Champion CI/CD, observability, testing, and infrastructure-as-code
Lead design sessions with engineers and architects and translate product requirements into maintainable technical solutions
Participate in code reviews and knowledge sharing
Mentor mid-level and junior developers

Requirements

What you’ll need

Must be eligible to work in the US without Visa Sponsorship
6+ years of professional backend or full-stack engineering experience with a deep Python/React focus
Strong Python and React foundation, including async patterns, type annotations, testing, and production-grade service/component design
Proven experience designing and deploying cloud-native applications; GCP strongly preferred, AWS or Azure considered
Hands-on experience building resilient, high-throughput microservices and RESTful/gRPC APIs
Understanding of Docker, Kubernetes, and serverless paradigms
Strong grounding in SOLID design principles and software craftsmanship
Experience working in cross-functional, agile teams
Comfort using AI tools to accelerate development throughput
Strong experience using and managing monorepos
Strong understanding of relational databases such as PostgreSQL, MySQL, or Oracle
Strong understanding of non-relational databases such as MongoDB or DynamoDB
Experience with search platforms such as Elasticsearch, OpenSearch, Solr, or Algolia is strongly preferred
Familiarity with vector search concepts and tools such as embeddings, ANN, FAISS, Pinecone, or Weaviate is strongly preferred
Exposure to RAG pipelines, LLM integration, prompt engineering, or fine-tuning workflows is strongly preferred
Experience with LangChain, LangGraph, or Google ADK is strongly preferred
Infrastructure-as-code experience with Terraform, Pulumi, or OpenTofu is strongly preferred
Licenses and certifications: None required

Benefits

Comp & perks

Options for healthcare coverage
401(k)
Tuition reimbursement
Vacation pay
Sick pay
Holiday pay
Hybrid work arrangement