Bolder Apps
Bolder Apps
51 – 200 Employees
ConsultingHealthcareMarketing
Bolder Apps is an AI-focused mobile and web app development agency headquartered in Miami, partnering with startups and businesses worldwide. Its team supports the full product lifecycle, from strategy and UI/UX design to native and cross-platform mobile development with Flutter, Swift, and Kotlin, as well as web development using React, Node.js, and Laravel. The agency also provides backend architecture, growth optimization, staff augmentation, ongoing support, and AI integrations. Bolder Apps works across fintech, healthcare, e-commerce, social media, and lifestyle projects, making it relevant to candidates interested in product engineering, client services, and applied AI development.

LLM Application Engineer - Argentina Remote

Build and operate production-grade LLM pipelines for Bolder Apps, an AI product studio. Lead extraction, evaluation, reliability, and cost optimization across multimodal workflows.

Description

  • Lead end-to-end production LLM pipelines spanning ingestion, multimodal model calls, structured records, storage, confidence scoring, retries, and idempotent rescans.
  • Develop prompt and schema strategies, including constrained or schema-aligned outputs, to produce consistent, product-ready results.
  • Create classification and filtering systems with taxonomy mapping, demographic or audience criteria, deduplication, and data cleanup.
  • Build evaluation harnesses using golden datasets, regression testing, and live production metrics.
  • Track quality against targets for completeness, duplicate rates, incorrect inclusions, image availability, and related product SLAs.
  • Reduce latency and spending through token controls, model tier selection, caching, and batching.
  • Strengthen long-running asynchronous jobs with timeouts, partial recovery, memory controls, and safe production releases.
  • Work with Flutter/mobile and QA teams on field contracts, review workflows, and incident investigation.
  • Maintain architecture documentation and operational runbooks that support shared ownership.
  • Monitor Gemini and comparable LLM APIs, recommending model changes, fallback strategies, and schema refinements.

Requirements

  • Demonstrated experience delivering production LLM applications, including prompting, structured outputs, retries, observability, and failure recovery.
  • Advanced hands-on experience with Google Gemini for multimodal text, image, and document workflows and structured extraction.
  • Practical experience with at least one additional major LLM platform, such as OpenAI or Anthropic.
  • Experience extracting structured information from HTML, PDFs, images, and mixed email-style content.
  • Experience developing classification and taxonomy systems driven by LLM outputs.
  • Strong evaluation practice covering offline tests, regression suites, and production metrics tied to defined acceptance criteria.
  • Understanding of cost and latency controls, including token budgets, lower-cost model tiers, caching, and batching.
  • Python backend experience with serverless cloud platforms such as Cloud Functions and document databases such as Firestore, or comparable GCP patterns.
  • English proficiency at C1 level or higher.
  • Availability for working-hours overlap through approximately 5 PM EST when live coordination is required.
  • A strong ownership mindset, including realistic estimates, early escalation of blockers, and completed releases.
  • Preferred experience includes schema-aligned LLM frameworks, Google Document AI or comparable OCR/document intelligence, Gmail API/OAuth compliance, computer vision, Firebase/GCP operations, production evaluation datasets, or agency and multi-client studio work.

Benefits

  • Fully remote, async-friendly work with overlap through approximately 5 PM EST when client or release coordination requires it.
  • Monthly retainer engagement focused on recurring AI pipeline work and maintaining production quality and cost control.
  • Substantial autonomy over prompt design, schemas, evaluation methods, and deployment practices.
  • Direct collaboration with project managers, mobile engineers, and decision-makers.
  • Budget for the LLM and cloud tools needed to deliver effectively.
  • Access to a peer network of product-focused builders working across related client projects.

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