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.