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.