• Embed & Discover: Audit real workflows in your assigned department. Find the "Human API" moments where smart people are doing robotic work (copy-pasting, manually reconciling data). • Provide the "Golden Start": Build default, pre-configured AI setups for your department teammates. Don't make an accountant figure out how to connect an LLM to NetSuite; build the integration, secure the endpoints, and hand them the keys. • Ship Foundational Wins: Build and deploy initial AI-powered automations using DoiT's standard stack (Next.js, TypeScript, Firestore, Cloud Run, and Claude API) to build trust and show what's possible. • Educate & Enable: Train your department teammates on the tools you build. Foster their growth so they can start writing their own prompts, configuring their own agents, and automating their own workflows. • Measure Outcomes: Track the time and cost savings for every automation shipped in your department. No vanity metrics, just real business outcomes.

We Make Remote Work Remarkable • TopTalent from LatAm Hello! We are GoFasti, a Talent-as-a-Service. GoFasti bridges the gap between world-class developers and designers from LatAm and first-class companies around the globe. We need an English-fluent AI/RAG Engineer, based in Latin America, available to work remotely. We are looking for someone with exceptional communication and relationship-building skills, who embraces changes while maintaining strong attention to detail. An interested and proactive person, who's constantly learning and improving their skills. Are you the one we are looking for? Responsibilities: - Architect and implement scalable AI integration patterns (RAG pipelines, agentic workflows, MCP tools, structured extraction, summarization, compliance intelligence) that can be reused across the platform - Design and deliver production-grade AI-powered features embedded within mission-critical procurement workflows, including internal tooling - Define and implement evaluation frameworks, observability, and guardrails to ensure model quality, cost governance, security, and compliance - Partner with architecture leadership to evolve our legacy monolithic platform toward more modular, service-oriented capabilities - Establish engineering standards for AI systems, including testing strategies, prompt/version management, model lifecycle practices, and performance monitoring - Collaborate cross-functionally with Product, UX and domain subject matter experts (internal and external) to ensure AI solutions solve real user problems - Contribute to architectural reviews and technical design sessions to ensure alignment with long-term platform modernization goals - Use PlanetBids issued AI tools and platforms to seek efficiencies in your role - Produce clear technical documentation to support maintainability and knowledge sharing. Requirements: - 3–5 years of software engineering experience, with exposure to AI/LLM-based solutions - Hands-on experience with LLM integrations (e.g., RAG, prompt engineering, summarization, or structured data extraction) - Proficiency in Python, Node.js, or similar, with experience building APIs and scalable services - Experience with cloud platforms (AWS, GCP, or Azure) - Familiarity with vector databases or retrieval systems (e.g., Pinecone, FAISS, etc.) - Solid understanding of modular or service-oriented architectures - Experience with monitoring, logging, and system performance in production environments - Ability to collaborate with cross-functional teams and translate business needs into technical solutions. Compensation: - The Salary range offered for this position varies from (USD) $3,500 - $4,500 per month, depending on seniority and skillset. - This position is for an independent contractor, through a payroll platform. - The talent will work REMOTELY allocated at our client. Here are the steps for this process: Application review/approval > Screening interview with GoFasti's team > We build and send your profile to our client > Profile review/approval by client > Interview with the client > Hiring and onboarding. Once you apply for the job, our team will review your resume. If it meets the requirements, we will contact you and move forward in the process. Note for Candidates Approached Directly: If you were contacted directly by a member of our team and are interested in this opportunity, please do not apply through this link. Instead, reach out to the person who contacted you to coordinate a meeting. Thank you!

• Implementing Azure OpenAI API for applications. • Implementing RAG systems. • Working with open source models to fine-tune and apply them for clients. • Experimenting with different ML techniques. • Creating PoC, MVP of AI applications. • Researching new techniques, models and approaches to apply AI in retail, healthcare, finance and general industries.

• Buscar um(a) Engenheiro(a) de IA que tenha paixão por soluções inovadoras e experiência prática no desenvolvimento de soluções de ponta. • Atuar em projetos de Inteligência Artificial Generativa no ecossistema Google Cloud Platform, colaborando com squads de desenvolvimento para construir, otimizar e escalar agentes autônomos e soluções de IA de última geração. • Desenvolvimento de Agentes: Projetar e desenvolver agentes autônomos inteligentes utilizando frameworks modernos como LangChain e LangGraph, criando fluxos complexos que combinem múltiplas ferramentas. • Arquitetura de Agentes: Implementar comunicação entre agentes utilizando arquiteturas alinhadas com a necessidade de negócios, integração de HiTL (Humans in The Loop), gerenciar o ciclo de vida e orquestração de agentes e integrar com ferramentas externas utilizando MCP servers. • IA Conversacional: Criar agentes conversacionais sofisticados com memória de curto e longo prazo, incluindo resumos de sessões anteriores, rastreabilidade de pautas e preferências do cliente. • Integração e Ecossistema Google: Utilizar o Google ADK, LangGraph e ou bibliotecas similares para construir, integrar e otimizar soluções de IA robustas. • Modelagem e Avaliação: Experimentar, integrar e avaliar diferentes LLMs (OpenAI, Gemini, DeepSeek, Cohere), utilizando frameworks de avaliação focados em LLM Evals para medir performance e garantir a qualidade dos produtos. • RAG e Dados: Implementar técnicas avançadas de RAG, incluindo Hybrid Search, reranking, e estratégias de auto-otimização como Corrective RAG (Self-Reflection), conectando os agentes a dados corporativos (ex: GCP) e estruturando bancos vetoriais. • Coleta de Informação: Utilizar mecanismos de web scraping inteligente e parsing de documentos para coletar e processar informações externas. • Automação e Pipelines: Integrar agentes a APIs (Requests, FastAPI) e serviços para executar ações e automações, além de montar pipelines de experimentação, versionamento e publicação de modelos. • Colaboração: Atuar junto às áreas de negócio para entender necessidades e transformá-las em soluções práticas utilizando agentes de IA.