T-Hub - AI Agents & LLM Engineer

Our requirements

• Minimum 5 years of experience in Software Engineering, Machine Learning, Data Science or AI-related roles.
• Strong hands-on experience designing and deploying AI agents and LLM-based applications in production environments.
• Deep understanding of Transformer architectures, RAG systems, embeddings, vector databases, prompt engineering, context management and agent orchestration.
• Expert-level proficiency in Python and modern AI frameworks.
• Strong experience with LangChain, LangGraph, SmolAgents, Google ADK, CrewAI or similar agent frameworks.
• Practical knowledge of building autonomous and multi-agent systems.
• Experience implementing advanced reasoning patterns, planning strategies and tool-augmented AI workflows.
• Strong understanding of software architecture, API development, microservices and distributed systems.
• Experience with SQL, data preprocessing, ETL pipelines, feature engineering and large-scale data processing.
• Experience with CI/CD pipelines and DevOps practices.
• Knowledge of MLflow, Airflow or similar MLOps platforms.
• Strong analytical, communication, and problem-solving skills.
• Fluent English communication skills, both written and spoken. Nice to Have:
• Experience with Generative AI evaluation frameworks and observability platforms (LangSmith, Arize, Galileo, Phoenix, etc.).
• Experience fine-tuning open-source models and working with model serving frameworks.
• Knowledge of graph-based AI architectures and knowledge graphs.
• Experience implementing AI governance, guardrails, and Responsible AI practices.

Your responsibilities

• Design, build and deploy solutions based on Machine Learning, LLMs and AI agents with a focus on enterprise production applications
• Develop and implement AI-powered applications utilizing RAG, agentic workflows, multi-agent architectures and tool-calling frameworks.
• Build and optimize data pipelines for training, validation and inference
• Optimize models for performance, scalability and cost-effectiveness
• Build scalable and cost-effective AI solutions leveraging cloud-native architectures.
• Conduct integration with applications (FastAPI, microservices)
• Implement MLOps and LLMOps processes, including automated training, testing, deployment, monitoring, evaluation, guardrails, and model retraining.
• Design agent orchestration frameworks and advanced reasoning workflows for autonomous and semi-autonomous AI systems.
• Integrate different AI models into production systems working closely with other engineering teams
• Collaborate with data science, POs and IT teams to define requirements into implementation
• Ensure security, compliance, reliability, and observability of deployed AI solutions.

What we offer

Benefits

• sharing the costs of sports activities
• private medical care
• sharing the costs of foreign language classes
• sharing the costs of professional training & courses
• life insurance
• flexible working time
• corporate products and services at discounted prices
• integration events
• mobile phone available for private use
• no dress code
• parking space for employees
• extra social benefits
• sharing the costs of tickets to the movies, theater
• holiday funds
• birthday celebration
• sharing the costs of a streaming platform subscription
• employee referral program
• charity initiatives
• extra leave
• Benefit Platform

Recruitment stages

• 1.
  Let’s meet to better understand each other's expectations.
• 2.
  Hiring Manager will receive your application after meeting.
• 3.
  Technical meetings with the Hiring Manager and/or team.
• 4.
  Time to decide!

T-Mobile