Senior LLM / Generative AI / Agentic Solutions Engineer

  • 📍
    Hely: Budapest
    Budapest, Hungary
  • 💰
    Munkabér ajánlat: Nincs megadva
  • 🏢
    Vállalat: Randstad Hungary Kft
Randstad

Cégleírás / Organisation/Department

Our partner is a fast-growing, innovation-driven company building and deploying AI solutions across Space, Manufacturing, AdTech, and FinTech. They combine state-of-the-art research with robust engineering to solve real-world problems at production scale.

Pozíció leírása / Job description

  • Build Agentic Systems: Design supervisor/executor patterns, memory strategies, and robust tool-calling failure handling.

  • LLM Adaptation & Deployment: Fine-tune open-source models and optimize inference for production-scale latency and cost.

  • Advanced RAG: Implement high-performance embedding, retrieval, and re-ranking pipelines for grounded outputs.

  • Structured Generation: Enforce schemas and guardrails to minimize hallucinations and ensure reliable system behavior.

  • Evaluation & Quality: Develop automated evaluation harnesses, regression tests, and versioning for prompts and models.

  • Production Engineering: Ship containerized APIs with full CI/CD, observability, and reliability monitoring (SLOs).

  • Cross-functional Delivery: Collaborate with product teams to integrate GenAI features and mentor junior engineers.

Elvárások / Requirements

  • Senior AI Expertise: 5+ years building production ML/AI systems, including 2+ years in lead roles with strong Python engineering (performance, testing, packaging).

  • LLM & Agentic AI: Hands-on experience with orchestration, tool-calling, and workflow integration, including LLM adaptation (PEFT/LoRA) and safety engineering.

  • Production RAG & Data: Proven track record of operating RAG pipelines, vector databases, and retrieval performance tuning in production.

  • MLOps & Cloud: Proficiency in containerized services (REST/gRPC), CI/CD, and monitoring within cloud environments (AWS/GCP/Azure).

  • Advanced Optimization: Experience in inference optimization (vLLM/quantization), event-driven orchestration, and automated evaluation (LLM-as-judge).

Amit kínálunk / Offer

  • Career Growth

  • Collaborative Team

  • Exciting Projects

  • Remote Work

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