Forward Deployed AI Engineer
R
RevoDataData and AI
Employment in HungaryFull-Time
Salary2,000,000 - 2,700,000 HUF per month
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Job Details
- Required Skills
- AWSPythonDatabricksMLOpsGenerative AI
Requirements
- Have experience across the AI system lifecycle, from experimentation to deployment and monitoring.
- Have built reliable AI systems that run in production.
- Have built production-grade GenAI applications, including RAG architectures, agentic workflows, and fine-tuning.
- Have experience with platforms such as OpenAI, Anthropic, or Azure AI, or similar.
- Have used GenAI frameworks such as LangChain, LangGraph, Pydantic AI, Hugging Face, or DSPy.
- Be able to define evaluation baselines and metrics and use them to measure system changes.
- Understand LLMOps and MLOps, including experiment tracking, model versioning, observability, and deployment.
- Know how to build automated pipelines for monitoring, evaluation, and CI/CD for ML and LLM systems.
- Have experience working with data and designing scalable training and inference pipelines.
- Have extensive Python experience and familiarity with data and ML libraries; write clean, testable, well-structured code.
- Have deployed or worked with AI solutions in AWS, Azure, or GCP.
- Enjoy client-facing consulting, translating business challenges into technical roadmaps and leading projects from scoping to delivery.
Responsibilities
- Embed with clients to design and build AI solutions for real business problems.
- Take AI systems from early experimentation through deployment and production monitoring.
- Build production-grade GenAI applications using RAG architectures, agentic workflows, and fine-tuning.
- Establish evaluation baselines and metrics, then iterate on prompts, retrieval, models, and parameters.
- Manage AI system lifecycles, including experiment tracking, model versioning, observability, and deployment.
- Build automated pipelines for monitoring, evaluation, and CI/CD for traditional ML and LLMs.
- Design training and inference data pipelines that scale and run reliably in production.
- Deploy or work with AI solutions in cloud environments such as AWS, Azure, or GCP.
- Translate business challenges into technical roadmaps and lead projects from scoping to delivery.
- Explain architectural decisions to clients and collaborate with engineers to debug pipelines.
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