Senior AI Engineer

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High Tech Engineering Center Kft.
Munkavégzés helye

Budapest

Munkaidő, foglalkoztatás jellege
- Alkalmazotti jogviszony
- Általános munkarend
Elvárt technológiák
- PYTHON AZURE API DATABASES DEBUGGING CI
Elvárások
- Angol középfok
- 1-3 év tapasztalat
- Középiskola

Állás elmentve

A hirdetést eltávolítottuk a mentett állásai közül.

Responsibilities

Chunking and ingestion architecture with deterministic, validation-gated pipelines handling diverse enterprise document types with indexing and contextual enrichment
Retrieval pipeline with vector search, BM25 hybrid retrieval, RRF re-ranking, embedding model selection and optimisation
Agent orchestration designing stateful, multi-step agent workflows with LangGraph and building chains and tool-use patterns with LangChain
Observability with tracing, debugging, and monitoring pipeline performance end-to-end with LangSmith and catching retrieval and reasoning failures before they reach users
Model evaluation and tuning with RAGAS-based evaluation frameworks, benchmarking retrieval recall and answer faithfulness, iterating toward production quality
Open-weight LLM deployment evaluating and shipping models (Llama 4, Qwen3, MiMo-V2) under memory and latency budgets via Amazon Bedrock and self-hosted inference
Workflow orchestration with async pipeline design using Temporal.io and containerised deployment on Kubernetes via GitLab CI

Requirements

Strong software engineering skills in .NET and/or Python with experience building production-grade APIs and services
Applied algorithmic problem solving including search, ranking, clustering, and data extraction/transformation pipelines
Solid understanding of LLM architectures and trade-offs (e.g. GPT-family, Claude, Llama, and similar models)
Experience integrating LLM and agent APIs (e.g. Azure OpenAI, OpenAI API, Claude API, Amazon Bedrock)
Design and implementation of agent-based systems for automation, orchestration, and decision support
Experience working with relational, document, and graph databases including data modeling and querying
Web data ingestion experience including web scraping, schema extraction, and handling changing source structures
Experience building reliable, scalable, maintainable AI systems including logging, error handling, and performance considerations

Nice-to-have

Fine-tuning and preference optimization techniques (e.g. supervised fine-tuning, RL-style approaches via APIs), Designing pipelines that convert unstructured data into reliable structured representations, Exposure to event-driven or background processing architectures for long-running AI workflows, Experience contributing to or operating AI-powered systems in production