AI/MLOps Engineer

Senior AI/MLOps Engineer

Domain: Telecommunications | AI & Machine Learning | Advanced Security
Location: Hybrid, Bucharest (flexible)
Contract: Preferred employment contract (CIM), but B2B is also possible

We are looking for a AI MLOps Engineer with top-tier experience, passionate about customizing large language models (LLMs) for advanced security analysis of telecommunications equipment. The ideal candidate has solid expertise in MLOps (machine learning operations) and data engineering, transforming raw data into meaningful insights through fine-tuning, RAG, and deploying solutions into production environments.

Required Technical Skills

• Proven experience in MLOps and the full ML lifecycle (data setup, processing, training, validation, deployment, monitoring)

• Strong proficiency in Python and ML/DL frameworks (PyTorch, TensorFlow, Hugging Face)
• Advanced knowledge of RAG and LLM fine-tuning
  
• Experience with Linux, Docker, Kubernetes, CI/CD for ML workflows
  
• Ability to design innovative ML architectures tailored to unique business and operational needs
• Excellent problem-solving, documentation, and cross-functional collaboration skills

Preferred Qualifications

• Experience in telecom security auditing and vulnerability remediation

• Familiarity with telecom protocols and network security data formats (PCAP, firewall logs, syslogs, SNMP, NetFlow, etc.)
• GPU/TPU acceleration and distributed training

• AI experience in regulated industries

Responsabilities

• Ingest and preprocess raw data (documents, PCAPs, CVEs, process logs, event logs, telecom traces, firewall logs, configuration files)
• Design and implement advanced RAG pipelines for highly specialized LLM use cases in telecom vulnerability analysis and remediation
• Develop and configure ML environments (cloud and on-premises) to support data processing, training, validation, and deployment
• Architect, fine-tune, and optimize LLMs for understanding, analyzing, and detecting vulnerabilities with high accuracy in telecom systems
• Apply best practices in MLOps for scalable, secure, and automated model lifecycle management
• Experiment with and evaluate innovative model architectures to improve performance and domain adaptation
• Ensure models meet commercial deployment requirements, including security, compliance, and performance standards
• Collaborate with cybersecurity and telecom experts to refine model outputs for operational use