Company hidden

2 дня назад

Senior AI Engineer

Формат работы

onsite

Тип работы

fulltime

Грейд

senior

Английский

Страна

Netherlands

Вакансия из Hirify Global, списка международных tech-компаний
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Мэтч & Сопровод

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Описание вакансии

Текст:

TL;DR

Senior AI Engineer (LLM/RAG/MLOps): Build production-ready AI and data products, including LLM applications, retrieval-augmented generation pipelines, agentic workflows, and scalable ML model services with an accent on reliability, evaluation, data quality, and cloud infrastructure. Focus on productionizing models, automating accuracy and safety guardrails, building ML pipelines, and operating low-latency services for global traffic.

Location: Amsterdam, The Netherlands

Company

hirify.global develops mapping and location technology products.

What you will do

Design, develop, and maintain production AI systems, including LLM applications, RAG pipelines, agentic workflows, and ML model services.
Productionize models and prototypes as robust, low-latency, cost-efficient services that scale to global traffic.
Define and automate evaluation frameworks, benchmarks, guardrails, and production monitoring for accuracy, safety, latency, and cost.
Build and optimize data pipelines for training, fine-tuning, embeddings, and inference while maintaining data quality, traceability, and privacy compliance.
Implement ML CI/CD, model versioning, experiment tracking, observability, and cloud infrastructure.
Collaborate with product, engineering, and science stakeholders, evaluate new AI technologies, and share technical knowledge.

Requirements

Bachelor’s degree in Computer Science, Engineering, AI/ML, or a related field, or equivalent professional experience.
Strong proficiency in one or more high-level programming languages such as C++, Java, Python, or similar.
Hands-on experience with LLMs, prompt engineering, RAG, embeddings, vector databases, tool use, and agent frameworks.
Solid understanding of software architecture, API design, design patterns, and maintainable, scalable systems.
Experience with cloud providers such as Azure, AWS, or GCP; Docker; Kubernetes; and CI/CD tools.
Knowledge of version control systems, preferably Git.
Excellent problem-solving and communication skills, with the ability to work effectively in cross-functional teams.

Nice to have

Experience with fine-tuning, ML frameworks and libraries such as PyTorch, TensorFlow, or Hugging Face.
Familiarity with MLOps practices and tools, including MLflow, experiment tracking, and model monitoring.
Experience with geospatial, mapping, or location data.

Culture & Benefits

Cross-functional collaboration with product, engineering, and science stakeholders.
Opportunities to evaluate emerging AI models, tools, and techniques and share knowledge with the engineering community.

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