2 дня назад
Senior Staff Engineer - Data Science (AI)
Мэтч & Сопровод
Для мэтча с этой вакансией нужен Plus
Описание вакансии
Текст:
TL;DR
Senior Staff Engineer - Data Science (AI) (AWS Bedrock/LLMs): Building and modernizing enterprise Generative AI applications while supporting production systems with an accent on foundation models, retrieval-augmented generation, vector search, and responsible AI. Focus on designing solutions, developing high-quality Python services, resolving production incidents, and implementing testing, monitoring, and deployment processes.
Location: Guadalajara, Mexico
Company
Digital Product Engineering company delivering products, services, and digital experiences across devices and mediums.
What you will do
- Provide KTLO support for current implementations and collaborate with the onshore team.
- Perform technical impact analysis, root cause analysis, incident management, and defect resolution.
- Design and develop modernization initiatives, new services, and enterprise Generative AI applications.
- Create proofs of concept, write high-quality code, conduct code reviews, and deploy features using approved tools and processes.
- Implement test automation, logging, monitoring, documentation, and process flow diagrams.
- Prepare status reports and maintain feature and defect tracking.
Requirements
- 8+ years of experience in Data Science, Machine Learning, AI, or related fields.
- 2+ years of hands-on experience with Generative AI and Large Language Models.
- Strong hands-on experience with AWS Bedrock and enterprise GenAI applications.
- Experience with foundation models, LLM APIs, prompt engineering, RAG, embeddings, vector search, semantic search, context management, LLM evaluation, and AI guardrails.
- Strong programming skills in Python.
- Experience with Pandas, NumPy, Scikit-learn, PyTorch, or TensorFlow.
Culture & Benefits
- Full-time employment.
- Dynamic, non-hierarchical work culture.
- Collaboration with an international organization of more than 15,000 experts across 26 countries.
- Agile delivery practices focused on small, fast changes and short feedback loops.
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