AI Engineer Manager

🕒 Agosto 7

🇦🇷 Argentina – Remoto

⏰ Tempo Integral

🟡 Pleno

🟠 Sênior

🤖 Engenheiro de IA

👻 Score fantasma 27%

O que significa: quão provável esta vaga está aberta sem contratação ativa.Como ler: 60% ou mais significa que achamos que é uma vaga fantasma. Pontuações mais baixas preocupam menos.Como pontuamos: tempo em aberto, republicações, vagas abertas vs tamanho do time, rapidez para fechar vagas, e se o salário está divulgado.Observação: é uma estimativa de dados públicos, não um julgamento da empresa. Vagas reais podem ter pontuação alta, então use como uma dica, não como um fato.

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Blend360

SiteLinkedInTodas as Vagas

501 - 1000 funcionários

🏥 Saúde

🏨 Hospitalidade

✈️ Turismo

💰 $100.000.000 Private Equity Round em 2022-08

Healthcare • Hospitality • Travel

A Blend360 é uma empresa de serviços profissionais especializada em soluções de IA, análise de dados e soluções orientadas por dados. Eles trabalham com empresas da Fortune 1000 e grandes marcas empresariais para enfrentar desafios significativos, integrando pessoas e inteligência artificial. A Blend360 foca em vários domínios, incluindo inteligência de negócios, engenharia de dados, ciência de dados, MLOps e governança de dados. Suas indústrias de expertise englobam serviços financeiros, energia, saúde e ciências da vida, varejo, tecnologia, mídia & telecomunicações e viagens & hospitalidade. A Blend360 é reconhecida por suas soluções de IA e dados, tendo recebido prêmios como "Solução do Ano em Capacitação de IA" e sendo listada entre os "Principais Provedores de Serviços de IA Generativa de 2024.

Descrição

• Lead AI project delivery end to end, ensuring clear governance, strong stakeholder communication, and reliable execution • Design and build robust RAG systems, agentic frameworks, and LLM-powered solutions for production environments • Apply advanced prompt engineering techniques, including instruction design, few-shot prompting, structured outputs, and tool/agent prompts • Lead feasibility assessments across prompting, RAG, fine-tuning, classical ML, and hybrid solutions • Design evaluation frameworks using LLM-as-a-judge, custom metrics, recall@k, precision@k, and go/no-go gates • Run structured experiments across prompts, retrievers, chunking strategies, embeddings, reranking approaches, and models • Identify and categorize model failures, including hallucinations, retrieval misses, instruction-following errors, and quality regressions • Build scalable inference infrastructure and CI/CD pipelines for AI and ML models • Automate the MLOps/LLMOps lifecycle, including tracking, versioning, deployment, monitoring, retraining, and continuous improvement • Design APIs, microservices, and orchestration layers optimized for latency, cost, reliability, and scalability • Mentor junior engineers • Contribute to proposals, solution design, and new business initiatives • Communicate with engineering teams, senior stakeholders, and clients • Help clients make pragmatic decisions about AI system capabilities

🎯 Requisitos

• 6+ years of professional experience building and deploying AI, ML, or data-driven/software solutions in production environments • Strong expertise in Python • Solid Git practices • Hands-on experience with LLM-powered solutions, RAG systems, and modern GenAI development patterns • Practical experience with chunking, embeddings, retrieval, reranking, and evaluation • Strong understanding of prompt engineering, including structured outputs, few-shot prompting, instruction design, and tool/agent prompts • Proven experience designing AI evaluation strategies, including metrics, dataset curation, structured experimentation, and quality gates • Experience with MLOps/LLMOps tools such as MLflow, Weights & Biases, or similar platforms • Solid cloud experience with AWS, Azure, or GCP • Experience with containerization, orchestration, scalable inference, APIs, and microservices • Understanding of event-driven architectures and production-grade engineering practices • Clear communication with engineering teams, senior stakeholders, and clients • Pragmatic approach balancing innovation, reliability, cost, latency, and business value • Advanced English level required for written and verbal communication • Azure experience preferred • Nice to have: Databricks MLOps platform, LLM fine-tuning, agentic GenAI systems, Infrastructure as Code, security and observability practices for AI services, classical machine learning, open-source contributions or public technical work

🏖️ Benefícios

• Flexible working options • All the equipment you need to harness your talent (Macbook and accessories) • Every day lunches at headquarters, with vegetarian, vegan, gluten and sugar free options • Gourmet meals every Friday with an on-site chef at headquarters • Snacks and beverages available every day at headquarters • After office events, football, tennis and game nights at headquarters • Football league, pool games, tennis courts, chess championships, game and music nights • AWS certifications, study plans, courses and other certifications • English lessons • Learning from teammates on Tech Tuesdays • Mentoring and development opportunities • Anniversary and birthday gifts