Technical Lead, Artificial Intelligence

What you'll do:

• Define the end-to-end architecture for AI/ML systems, including agent orchestration, model serving, data pipelines, RAG, and evaluation infrastructure.
• Drive technical decisions around models, frameworks, deployment strategies, and build-vs-buy approaches.
• Prototype and implement critical components while setting engineering and code-quality standards.
• Lead AI solutions from prototype to production, including CI/CD, monitoring, model lifecycle, versioning, and rollback.
• Establish evaluation frameworks, benchmarks, and safety criteria for AI systems operating on network data.
• Mentor engineers through architecture discussions, design reviews, and code reviews.
• Collaborate with RAN Systems, PHY, L2/L3, Product, and customer-facing teams to translate network challenges into practical AI/ML solutions.
• Contribute to technical roadmap discussions and customer-facing architecture discussions.

What you should have:

• 7+ years of experience in software or ML engineering, with significant experience delivering production systems.
• Proven technical leadership and experience owning the architecture of complex systems end to end.
• Strong Python skills and hands-on experience with PyTorch or similar ML frameworks.
• Practical experience with LLM-based systems, including agents, tool calling, RAG, orchestration, prompt/context engineering, and evaluation.
• Strong understanding of classical ML, including time-series analysis, anomaly detection, and supervised learning.
• Working knowledge of 4G/5G RAN architecture, L1/L2/L3, network KPIs, and cellular network operations.
• Experience with MLOps, containers, CI/CD, experiment tracking, model monitoring, and production deployment.
• Excellent English and strong technical communication skills.

Nice to have:

• Hands-on experience in RAN, wireless infrastructure, telecom operators, or chipset companies.
• Knowledge of O-RAN, RIC, rApps/xApps, and E2/A1/O1 interfaces.
• Experience with link adaptation, scheduling, RRM, channel modeling, or PHY simulation.
• Experience with reinforcement learning or contextual bandits for real-world control problems.
• Experience deploying ML models in real-time or resource-constrained environments.
• Background in signal processing, communications, or information theory.
• M.Sc. / Ph.D. in Computer Science, Electrical Engineering, Applied Mathematics, or a related field.