6 дней назад
Lead AI Engineer
Мэтч & Сопровод
Для мэтча с этой вакансией нужен Plus
Описание вакансии
Текст:
TL;DR
Lead AI Engineer (AI/RAG/LLMOps): Leading the delivery of production AI systems, including RAG systems, agentic frameworks, and LLM-powered solutions, with an accent on team leadership, evaluation quality, and scalable infrastructure. Focus on designing experimentation frameworks, building MLOps/LLMOps pipelines, and optimizing APIs and microservices for latency, cost, and reliability.
Location: Buenos Aires, Argentina
Company
is an AI services provider that combines data science, artificial intelligence, technology, and human expertise to deliver client solutions.
What you will do
- Lead end-to-end delivery of AI projects, including governance, stakeholder communication, technical reviews, and architectural assessments.
- Build and mentor an AI engineering team, establish technical standards, and support hiring and team development.
- Design and deliver production-ready RAG systems, agentic frameworks, and LLM-powered solutions.
- Define and run evaluations across prompts, retrievers, chunking strategies, models, datasets, and quality metrics.
- Build scalable inference infrastructure, CI/CD pipelines, and automated MLOps/LLMOps lifecycle processes.
- Design APIs, microservices, and orchestration layers optimized for latency, cost, and reliability.
Requirements
- 5+ years of hands-on AI/ML engineering experience in production environments.
- 2+ years of direct team leadership or technical management experience.
- Expert Python proficiency, strong Git practices, and experience with ML/LLM versioning and deployment.
- Cloud experience with AWS, Azure, or GCP, plus containerization and orchestration knowledge.
- Hands-on RAG experience covering chunking, embeddings, retrieval, reranking, and evaluation.
- Advanced English required for communication with global teams and client leadership.
Nice to have
- Databricks MLOps, LLM fine-tuning, or agentic GenAI systems experience.
- Infrastructure as Code, security, and observability for AI services.
- Classical ML background or open-source contributions.
Culture & Benefits
- Flexible working options.
- Access to AI learning paths, study plans, courses, Udemy Business, and role-specific certifications.
- Certifications and learning opportunities in AWS, Databricks, and Snowflake.
- Career development plans, mentorship programs, and English lessons.
- Company-provided equipment and opportunities to attend industry conferences and meet clients.
Hiring process
- Submit a resume or LinkedIn profile for consideration.
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