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AI/Data Engineer – Core Role at WickedFile

Location: Remote.
Employment Type: Full-Time.
Experience Level: Mid-to-Senior.
Industry: Automotive SaaS, AI-Driven Analytics.

WickedFile is on a mission to redefine how automotive repair shops understand and act on their financial and operational data. We're turning messy, inconsistent data into insight that drives real-world results. Our platform leverages LLMs and AI to surface trends, identify outliers, and spot the invisible patterns that matter most to shop owners and multi-location operators.

We’re looking for a hands-on AI/Data Engineer who’s ready to take the lead on all things LLM + data processing. This is a mission-critical seat on a lean, fast-moving, highly technical team. You’ll work directly with leadership to build, deploy, and scale the systems that make WickedFile work.

What You’ll Do:

Interface with remote LLMs (OpenAI, Anthropic, Claude, Gemini, etc.) for document parsing, semantic matching, and complex reasoning tasks.
Deploy and fine-tune local LLMs (Llama, Mistral, etc.) for specialized tasks where latency, privacy, or cost demands it.
Train custom LLMs on proprietary datasets to increase task accuracy and context awareness.
Build and maintain robust data pipelines to clean, standardize, and normalize messy vendor and financial data from dozens of disparate systems.
Design prompt engineering strategies and feedback loops to improve reliability and accuracy of model outputs.
Work in AWS to deploy scalable and cost-efficient services across EC2, Lambda, S3, ECS, etc.
Own your output – From prototype to production, you’ll be expected to think in terms of performance, security, cost, and maintainability.

What You Should Bring:

Proven experience working with LLMs via API (OpenAI, Claude, Gemini, etc.) and fine-tuning or training smaller models (e.g. Llama, Mistral, etc.).
Strong background in Python or Node.js, including working with libraries like LangChain, Transformers, or similar.
Deep understanding of data wrangling, cleansing, and normalization techniques across unstructured and semi-structured datasets.
Comfortable with AWS – deploying inference pipelines, managing scalable workloads, and optimizing compute/storage costs.
Knowledge of prompt engineering techniques, context management, and token budgeting.
Bonus: Familiarity with automotive service or financial data, or experience parsing documents like invoices/statements.

Why Join WickedFile:

Massive Impact – Your work will directly improve business decisions at real shops across North America.
High Ownership – No silos. You’ll have autonomy and a voice in shaping core tech and product direction.
Ground Floor – Be part of something early. We’re small, nimble, and moving fast.
Real-World AI – Not a research role. We’re shipping LLM-powered features used in production by paying customers.

Ready to build something that actually matters?
Send us your resume, GitHub, or a few words about what you've built. Let’s talk.

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