Clinton Tractor & Implement Co.

Working Style & Expectations

We are looking for committed, highly motivated professionals who take ownership seriously. This role requires consistent collaboration during U.S. Eastern Time business hours, reliability in scheduling, and strong follow-through. Being on time, prepared, and diligent is essential — this is a senior role on a system that underpins real operations, not an experimental project.

We value people who are excited by hard problems, communicate clearly, and treat commitments as non-negotiable.

1. AI/ML Developer Model Training & Fine Tuning

2. Data Model & Architect

3. Developers Microservices / API  ETL & Integration developement

We are building an end-to-end system that turns unknown physical parts into known, sellable products — automatically. The system ingests parts through vision and sensors, resolves identity using AI and structured knowledge, and publishes those parts into a fully integrated commerce, logistics, and search ecosystem.

This is not a research role. It is a production systems role spanning computer vision, large-scale data ingestion, retrieval systems, cloud infrastructure, and e-commerce integration.

What you will build

• A learning ingestion pipeline that identifies unknown physical parts using images, 3D data, and metadata, and improves continuously through human confirmation
• Vision and retrieval systems using multimodal embeddings and vector search to match parts across large catalogs
• A RAG-based knowledge system that ingests, parses, and normalizes manufacturer PDFs, schematics, and parts manuals into APIs
• Event-driven services that publish identified parts into inventory, commerce, shipping, and ERP workflows
• Search-optimized part representations engineered to be discoverable by Google, analytics systems, and downstream AI models

Why this is hard — and interesting

• Parts are visually similar, worn, incomplete, and inconsistent across manufacturers
• Manufacturer data is fragmented, unstructured, and often only available as PDFs and diagrams
• The system must operate across perception, inference, search, commerce, and logistics — not just ML
• Identification must be probabilistic, explainable, and correctable, while downstream systems require deterministic truth
• The platform must learn continuously without breaking accounting, inventory, or customer trust

This is a real-world intelligence problem, not a model-training exercise.

Technical environment

• Google Cloud Platform (Cloud Run, Compute Engine, GPU instances, Cloud Storage)
• MongoDB Atlas for operational and catalog data
• Vector search and retrieval systems for embeddings and similarity matching
• Event-driven and API-first architecture
• Computer vision, multimodal embeddings, and RAG pipelines in production

Required experience

• Senior-level experience building production AI or data-driven systems end-to-end
• Strong background in computer vision, retrieval systems, or multimodal ML
• Experience designing cloud-native systems on GCP or similar platforms
• Deep Python expertise; comfort owning architecture and trade-offs
• Ability to connect ML outputs to real business systems (commerce, ERP, logistics)

What success looks like

• Unknown parts can be ingested, identified, and published without manual data entry
• Manufacturer documents become structured, queryable knowledge
• Identified parts flow cleanly into e-commerce, search, and fulfillment systems
• The system improves over time without increasing operational risk

Looking for talent who thrive establishing new technologies and the challenges that come with that. 

This is development and go live of an entirely new platform and offering in the agriculture space working with big Ag manufacturers and data. 

You'll Own:

• ETL pipelines ingesting vendor pricebooks, inventory feeds, and product metadata
• Data normalization and enrichment workflows (JSON sidecar pattern)
• Multi-source data unification into MongoDB Atlas
• Image/metadata processing pipelines for ML-ready assets

Stack: GCP (Cloud Storage, Cloud Run), MongoDB Atlas, Python, JSON/XMP metadata

Must Have:

• 3+ years ETL/data pipeline experience
• Python proficiency
• Experience with document databases (MongoDB preferred)
• Comfortable with messy, real-world data normalization
•  

Nice to Have: Ecommerce catalog experience, CLIP/vector embeddings, agricultural/equipment industry

Required Skills & Experience

Must Have:

• 5+ years in SEO with proven organic traffic growth results
• 3+ years hands-on with Google Analytics (GA4 required, UA history fine)
• Expert-level Google Search Console usage
• Experience with technical SEO (JavaScript rendering, crawl optimization, structured data)
• Proficiency building dashboards in Looker Studio (Data Studio)
• SQL skills for querying BigQuery or similar
• E-commerce SEO experience (product feeds, category optimization, scale)
• Comfortable in Google Tag Manager

Technical Stack Familiarity:

• GA4 + BigQuery export and analysis
• Google Search Console API
• Looker Studio / Data Studio
• Google Tag Manager (server-side a plus)
• Screaming Frog, Sitebulb, or similar crawlers
• Ahrefs, SEMrush, or similar SEO platforms
• Basic Python or JavaScript for automation (preferred)
• GCP fundamentals (Cloud Run, Cloud Storage, BigQuery)

Attributes:

• Self-directed—you don't wait to be told what to analyze
• Strong communicator—can explain technical findings to non-technical team
• Detail-oriented but sees the big picture
• Comfortable with ambiguity and building processes from scratch
• Bias toward action over endless analysis

Nice to Have

• Experience with YouTube SEO
• Local SEO / Google Business Profile optimization
• E-commerce platform experience (Shopify, WooCommerce, custom)
• Exposure to AI/ML in search or content optimization
• Agricultural, equipment, or B2B industry experience
• Experience with MongoDB or Elasticsearch

What You'll Get

• Direct impact on a growing business ($10M+ and scaling)
• Ownership of the entire organic/analytics function
• Work with modern stack (GCP, AI tools, custom platform)
• Small team = no bureaucracy, fast decisions
• Flexible work arrangement
• Competitive salary + performance incentives

scaling from 10 parts to 100,000+ with AI-powered enrichment.

You'll Own:

• CI/CD pipelines and deployment automation
• GCP infrastructure (Cloud Run, Cloud Storage)
• API development and integration (UPS, Stripe, DIS/ERP)
• YOLO-based image recognition pipeline deployment
• Observability and monitoring foundation

Stack: GCP, MongoDB Atlas, Docker, Python, GitHub Actions

Must Have:

• 3+ years backend/DevOps experience
• GCP or equivalent cloud platform
• CI/CD pipeline design (GitHub Actions, Cloud Build)
• Container orchestration

Nice to Have: ML model deployment (YOLO/PyTorch), vector databases (Weaviate/Pinecone), legacy system integration

Looking for talent who thrive establishing new technologies and the challenges that come with that.

This is development and go live of an entirely new platform and offering in the agriculture space working with big Ag manufacturers and data.

AI-powered parts discovery for agricultural equipment.

You'll Own:

• Product detail pages (PDPs) with rich metadata display
• AI-powered search and discovery UI
• Category administration interfaces for staff
• SEO optimization and Google Search Console integration

Stack: Next.js, React, Vercel, TypeScript

Must Have:

• 3+ years JavaScript/TypeScript
• React/Next.js experience
• Strong CSS/responsive design skills
• SEO fundamentals

Nice to Have: Ecommerce UI experience, search UX, Tailwind CSS