Responsibilities
- Design, implement, and maintain production-grade AI systems, including traditional ML and LLM-based agentic solutions
- Own end-to-end ML and LLM pipelines, from data processing and feature pipelines to deployment, monitoring, and continuous improvement
- Build and operate efficient LLM inference and serving stacks, including deployment and optimization (e.g., batching, quantization, scalable runtimes, vLLM)
- Lead architectural and technical decisions, setting best practices, coding standards and mentoring engineers
- Collaborate closely with product managers, software engineers, and stakeholders to translate business needs into scalable AI solutions
- Ensure reliability, scalability, and observability of AI in production
- Contribute to and evolve the team’s MLOps processes, including CI/CD, automation, and model lifecycle management
Basic qualifications
- BSc in Computer Science, Electrical and Computer Engineering, or related field
- Proven experience delivering production AI systems as an ML / Software Engineer
- Strong understanding of machine learning and applied AI in production environments
- Hands-on experience with end-to-end ML and LLM pipelines and LLM-based systems (conversational AI, RAG, agentic workflows, vector databases)
- Experience deploying and optimizing LLMs in production, including inference tuning and efficient serving
- Solid experience with MLOps practices (CI/CD, model versioning, lifecycle management)
- Excellent proficiency in Python, plus experience in at least one additional production language (e.g., Java or C++)
- Experience designing scalable AI architectures and integrating them into existing products and platforms
- Experience with ML/AI frameworks (PyTorch, TensorFlow/Keras, Scikit-learn) and LLM orchestration tools (LangChain, LangGraph, etc.)
- Familiarity with containerized deployments (Docker, Kubernetes) and cloud platforms
- Strong problem-solving skills and fluency in English
- Familiarity with S/W development practices and verification frameworks (git, Gitlab, GitHub, CircleCI, Sonar, Jenkins, etc.)
Preferred qualifications
- Data engineering or large-scale data pipeline experience
- Knowledge of telecommunication networks and networking protocols
- Exposure to 5G RAN architecture is highly appreciated
- Experience working in regulated environments or with enterprise-grade systems
- Backend development (e.g., Django) and strong Linux networking knowledge
- Familiarity with observability tools (Prometheus, Grafana, ELK/OpenSearch)
- Strong ownership and accountability
- Excellent analytical and critical thinking skills
- Balance of strategic thinking and hands-on execution
- Clear communicator and collaborative team player
- Curious, proactive, and continuous learning with a passion for AI and emerging technologies
- Comfortable working in a dynamic environment with evolving requirements
- Continuous training and professional development to stay ahead of technological advancements.
- An equal opportunity workplace that values diversity, ensuring fair treatment regardless of ethnicity, nationality, religion, disability, gender, sexual orientation, union membership, political affiliation, or age.
Tags & focus areas
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