We're hiring a mid-senior LLM Application Engineer on a remote monthly retainer to design, ship, and harden production AI pipelines for client products at Bolder Apps. You'll own structured extraction and classification systems that turn messy real-world inputs (email, HTML, PDFs, images) into reliable product data, with measurable quality gates, evals, and cost control. You'll work on Firebase / GCP-style backends with product, mobile, and QA. We want someone who has shipped LLM apps for real users, not demos. You should be strong across modern LLMs and especially fluent with Google Gemini (multimodal prompts, structured outputs, failure modes, and cost/latency tradeoffs), with solid experience on other major providers too. If you can hit hard quality targets, keep dollars-per-run honest, and leave runbooks another engineer can pick up, we want to talk.
About Us
Bolder Apps is a product development studio that partners with US-based startups and established companies to build and scale innovative digital products. We specialize in AI-powered development, full-cycle product creation, and engineering team augmentation. Our mission is simple: build bolder, faster, and smarter.
Our Culture & Values (read before applying)
We move fast. We take ownership. We work with AI, not against it. And we expect everyone to bring ideas, not wait for instructions.
There are no daily checklists, no micromanagement, and no corporate politics. Instead, you'll have autonomy, trust, and a team that's always ready to help you grow. At Bolder Apps, impact matters more than titles, and curiosity matters more than seniority.
If you want a place where you can level up fast and actually see your work making a difference - welcome aboard.
Requirements
Responsibilities
- Own production LLM pipelines end to end: ingestion, multimodal model calls, structured records, and storage, including confidence flags, retries, and idempotent rescans
- Design prompt and schema strategies (including schema-aligned or constrained outputs) so results are consistent and product-ready
- Build classification and filtering layers on top of extraction (taxonomy mapping, demographic or audience filters, deduplication, and related cleanup logic)
- Define and run evaluation harnesses (golden sets, regression suites, online metrics) so quality does not regress when prompts, models, or parsers change
- Hit and report against hard quality targets (precision-style gates for completeness, duplicates, incorrect inclusions, image presence, and similar product SLAs)
- Optimize token usage, model tiering, caching, and batching to keep dollars-per-run and latency under control
- Harden reliability for long-running async jobs (timeouts, partial recovery, memory limits, safe production deploys)
- Partner with Flutter / mobile and QA on field contracts, review queues, and incident debugging
- Document architecture and runbooks so ownership is shared, not a single point of failure
- Stay current on Gemini and peer LLM APIs; recommend when to swap models, add fallbacks (e.g. document AI), or tighten schemas
- Shipped LLM applications in production (not demos only): prompts, structured outputs, retries, observability, and real failure handling
- Strong hands-on experience with Google Gemini, including multimodal (text + image / document-style) workflows and structured extraction
- Practical experience with at least one other major LLM stack (OpenAI, Anthropic, or similar) and good judgment on when to use which
- Structured extraction from messy inputs: HTML, PDFs, images, and mixed email-like content
- Classification / taxonomy systems on top of LLM outputs
- Evaluation discipline: offline evals, regression suites, and production quality metrics tied to clear acceptance criteria
- Cost and latency awareness: token budgeting, cheaper tiers, caching, batching; can explain dollars-per-run tradeoffs to a PM
- Python backend experience on serverless cloud (Cloud Functions or equivalent) and document stores (e.g. Firestore) or similar GCP patterns
- English at C1 or above for client-adjacent debugging with a PM
- US hours overlap through roughly 5 PM EST when live coordination is needed
- Ownership habits: honest estimates, early blockers, finished releases
Nice to have
- Schema-aligned LLM frameworks (BAML, Instructor, Outlines, or similar)
- Google Document AI or other OCR / document intelligence as a fallback path
- Gmail API / OAuth products and restricted-scope compliance familiarity
- Computer vision for product-image quality checks
- Firebase + GCP ops (secrets, regions, schedulers, cost monitoring)
- Building eval corpora from real production data and iterating until contractual SLAs pass
- Agency or multi-client studio experience
Benefits
- Fully remote and async-friendly, with required overlap through ~5 PM EST when client or release coordination needs it
- Monthly retainer structure with recurring AI pipeline work for engineers who keep production quality and cost honest
- Real autonomy over how you structure prompts, schemas, evals, and deploys. We do not hand you a rigid playbook
- Direct line to PMs, mobile engineers, and decision-makers
- Tooling budget for the LLM and cloud tools you need to move fast
- A peer network of product-minded builders across overlapping client projects
