LLM Application Engineer

Bolder Apps

. Own production LLM pipelines end to end, including ingestion, multimodal model calls, structured records, storage, confidence flags, retries, and idempotent rescans .

Posted 10/3/2026full-timeRemote • Argentina, Colombia, Mexico, Uzbekistan, GeorgiaMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in managing production LLM pipelines, including ingestion, structured outputs, and evaluation metrics. Proficient in optimizing model performance and collaborating with cross-functional teams to ensure quality and efficiency in LLM applications.

Highest-signal resume keywords

Production LLM ApplicationsGoogle Gemini ExperiencePython Backend DevelopmentStructured Extraction TechniquesEvaluation Discipline

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

LLM Pipeline ManagementPrompt DesignClassification SystemsTaxonomy MappingRegression SuitesToken BudgetingAsynchronous Job ManagementStructured Output GenerationCost OptimizationObservability

Soft Skills

Ownership HabitsCollaborationProblem-Solving

Tools & Technologies

Google Cloud FunctionsFirestoreGoogle Document AIGmail APIFirebase

Industry Keywords

Multimodal WorkflowsDocument IntelligenceProduction Quality MetricsAPI FamiliarityComputer Vision

Tech Stack

Tools & technologies

CloudFirebaseFlutterGoogle Cloud PlatformPython

About the role

Key responsibilities & impact

Own production LLM pipelines end to end, including ingestion, multimodal model calls, structured records, storage, confidence flags, retries, and idempotent rescans
Design prompt and schema strategies, including schema-aligned or constrained outputs, for consistent product-ready results
Build classification and filtering layers, including taxonomy mapping, demographic or audience filters, deduplication, and cleanup logic
Define and run evaluation harnesses with golden sets, regression suites, and online metrics
Report against quality targets for completeness, duplicates, incorrect inclusions, image presence, and related product SLAs
Optimize token usage, model tiering, caching, and batching to control cost and latency
Harden long-running asynchronous jobs with timeouts, partial recovery, memory limits, and safe production deployments
Partner with Flutter/mobile and QA teams on field contracts, review queues, and incident debugging
Document architecture and runbooks for shared ownership
Stay current on Gemini and peer LLM APIs; recommend model swaps, fallbacks, and schema improvements

Requirements

What you’ll need

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, and document-style workflows and structured extraction
Practical experience with at least one other major LLM stack, such as OpenAI or Anthropic
Structured extraction from HTML, PDFs, images, and mixed email-like content
Classification and 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, and batching
Python backend experience on serverless cloud, such as Cloud Functions, and document stores, such as Firestore, or similar GCP patterns
English at C1 or above
US hours overlap through roughly 5 PM EST when live coordination is needed
Ownership habits: honest estimates, early blockers, and finished releases
Nice to have: schema-aligned LLM frameworks, Google Document AI or other OCR/document intelligence, Gmail API/OAuth compliance familiarity, computer vision, Firebase/GCP operations, production evaluation corpora, and agency or multi-client studio experience

Benefits

Comp & perks

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
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