Senior AI Engineer

Feladatok, amikben számítunk Rád

• Agentic AI development: Design and implement autonomous and semi-autonomous AI workflows, including planning, task execution, tool integration, context management, and fault recovery.
• LLM development and optimization: Adapt and deploy open-source language models, apply parameter-efficient fine-tuning techniques, and optimize inference performance, latency, and operational costs.
• Retrieval-augmented generation: Develop and maintain RAG pipelines incorporating embeddings, document retrieval, reranking, and context grounding.
• Reliable AI outputs: Implement structured generation, output validation, safety mechanisms, and post-processing strategies to improve consistency and minimize hallucinations.
• Evaluation and quality assurance: Build automated testing and evaluation frameworks for language models and AI agents, including benchmark suites, regression testing, production monitoring, and version control for prompts and models.
• Production deployment: Develop containerized applications and APIs, establish CI/CD workflows, and implement monitoring, observability, alerting, and reliability practices.
• Technical collaboration: Partner with engineering, product, data, and infrastructure teams to integrate AI capabilities into existing systems and business processes. Contribute to technical decision-making and support the development of other engineers.

Várjuk jelentkezésed, amennyiben az alábbiakban magadra ismersz

• At least 5 years of experience developing and deploying production-grade AI, machine learning, or software systems, including 2+ years in a senior or technical leadership role.
• Strong Python programming skills, including software architecture, automated testing, performance optimization, and maintainable code design.
• Practical experience building and deploying LLM-powered applications and agentic workflows, including tool calling, orchestration, and integration with existing services.
• Experience adapting language models using techniques such as LoRA, QLoRA, PEFT, or comparable approaches.
• Hands-on experience designing and operating retrieval-augmented generation (RAG) solutions.
• Solid understanding of model evaluation, output quality, and AI safety considerations.
• Experience with MLOps practices, containerization, CI/CD pipelines, versioning, and production monitoring.
• Experience developing and maintaining APIs and backend services, including authentication, rate limiting, error handling, and resilience.
• Familiarity with at least one major cloud platform, such as AWS, Google Cloud, or Microsoft Azure.

What Success Looks Like

• Delivering reliable, scalable AI applications that solve practical business problems.
• Improving model quality, inference efficiency, and end-to-end system performance.
• Establishing repeatable evaluation and deployment practices for AI-powered products.
• Building maintainable systems that integrate seamlessly with existing technical environments.
• Contributing to engineering excellence through collaboration, knowledge sharing, and sound technical judgment.
• Ability to translate technical requirements into robust, maintainable, and production-ready solutions.

Kiválasztásod során előnyben tudunk részesíteni, amennyiben

• Experience optimizing LLM inference through quantization, batching, caching, or GPU-based serving technologies.
• Familiarity with model-serving frameworks such as vLLM, Text Generation Inference, or equivalent solutions.
• Knowledge of AI security practices, including prompt injection mitigation, secure tool execution, sandboxing, and adversarial testing.
• Experience with advanced evaluation methodologies, including model-based judging, rubric-driven assessment, preference evaluation, and A/B testing.
• Experience optimizing vector search systems, embedding pipelines, and retrieval performance.
• Familiarity with event-driven architectures and workflow orchestration platforms.
• Experience building multilingual AI applications and supporting internationalization requirements.