ML Search Engineer

Job Description

***DEPLOY has been retained by a leading industrial parts distributor for an on site role in Birmingham, Alabama. We are looking for an ML Search Engineer (Software Engineer III)***

You must be eligible to work in the US without Visa Sponsorship.

**ABOUT THE ROLE**

We're building intelligent product search that understands intent, learns from behavior, and gets smarter over time. As a **Senior Full Stack Engineer** on the **ML/AI Search team**, you'll design and build both the frontend and backend systems that power product discovery for millions of industrial buyers---from scalable retrieval pipelines, APIs, and Frontend interfaces that make AI accessible.

This isn't a research role. You'll own the full lifecycle: prototyping ideas, shipping production-grade services on GCP, and iterating based on real user data. Strong Python and React engineering is the foundation---if you also bring experience in search systems, vector databases, or Elasticsearch, you'll hit the ground running from day one.

**WHAT YOU'LL DO**

**Build \& Ship Search and AI-Powered Systems**

* 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: embedding models, transformer-based classifiers, LLM-powered query understanding, and reranking services.

* Develop event-driven, real-time architectures using GCP services---Cloud Run, Pub/Sub, GKE, Cloud Functions.

* Write clean, well-tested, observable Python backends; own your services through deployment, monitoring, and on-call

* Drive frontend architecture decisions, establishing development standards, and creating reusable component libraries.

**Contribute to Search Infrastructure**

* Work alongside the Search Architect and ML Architect to 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 (Pinecone, Weaviate, FAISS or similar) into retrieval workflows---even if this is new territory, you'll learn fast.

* Instrument search pipelines with meaningful metrics: CTR, zero-result rate, latency---feeding the team's A/B experimentation loop.

* Build clean, responsive, and production-ready interfaces from wireframes or Figma designs.

**Own the Engineering Bar**

* Champion CI/CD, observability, testing, and infrastructure-as-code as non-negotiables, not afterthoughts.

* Lead design sessions with Engineers and Architects; translate product requirements into clean, maintainable technical solutions.

* Participate in code reviews and knowledge-sharing---actively raising the team's collective skill level.

**WHAT YOU BRING**

**Must-Haves**

* **Strong Python and React foundation:**6 years of professional backend or full-stack engineering experience with a deep Python/React (e.g., Next.js, Vite.js, Remix.js, Gatsby.js) focus---async patterns, type annotations, testing, and production-grade service/component design.

* **Cloud-native experience:**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.

* Solid understanding of containerization (Docker), orchestration (Kubernetes), and serverless paradigms.

* Strong grounding in SOLID design principles and software craftsmanship.

* Good communicator who thrives in cross-functional, agile teams alongside ML engineers, architects, and product owners.

* Comfort using AI tools to accelerate development throughput.

* Strong experience of using and managing Monorepos

* Strong understanding of relational (e.g., PostgreSQL, MySQL, Oracle) and non-relational databases (e.g., MongoDB, DynamoDB).

* **Mentorship:** Provide guidance and technical knowledge sharing to mid-level and junior developers.

**Strongly Preferred --- Search \& ML**

*You don't need all of these on day one---but the more you bring, the faster you'll contribute:*

* **Search systems:**Experience with search platforms:

Elasticsearch, OpenSearch, Solr, or Algolia---index management, query DSL, relevance tuning.

* **Vector search:**Familiarity with vector search concepts and tooling:

embeddings, approximate nearest neighbor (ANN), FAISS, Pinecone, Weaviate, or similar.

* Exposure to ML/AI patterns: RAG pipelines, LLM integration, prompt engineering, or fine-tuning workflows.

* Experience with AI orchestration frameworks such as LangChain, LangGraph, or Google ADK.

* Infrastructure-as-code experience (Terraform, Pulumi, OpenTofu) and mature CI/CD pipeline ownership.

**WHO YOU ARE**

**Fearless Builder**

A working proof-of-concept beats a thousand slide decks. Speed and quality are both non-negotiable.

**Relentless Learner**

You pick up new technologies fast and have a genuine eagerness to master whatever comes next.

**Architecture-Minded**

You design scalable, fault-tolerant services as second nature and aren't afraid to challenge the status quo.

**Ownership-Driven**

Code isn't done until it's tested, documented, and monitored. Your name on a release means something.

**Search-Curious**

You may not have built a search system yet---but you're excited by the challenge and ready to go deep.

**Team Multiplier**

You give and welcome candid feedback and actively make the people around you better.