{"id":929346,"url":"https://alion.io/job/parallel-wireless-technical-lead-artificial-intelligence","title":"Technical Lead, Artificial Intelligence","company":{"id":4814,"name":"Parallel Wireless","domain":"parallelwireless.com","url":"https://alion.io/company/parallel-wireless","size_band":"501-1000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Lever","truth_index":{"grade":"B","score":78,"open_postings":19,"ghost_share":0.053,"stale_share":0.684,"repost_share":0.105,"time_to_fill_p50_days":50,"computed_at":"2026-11-06T05:58:05Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"lead","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Kfar Saba, Israel"],"countries":["IL"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":115000,"max_usd":286000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":1286},"experience_years_min":7,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Anomaly Detection","optional":false},{"name":"CI/CD","optional":false},{"name":"Context Engineering","optional":false},{"name":"Function Calling","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"RAG","optional":false},{"name":"Time Series","optional":false},{"name":"Reinforcement Learning","optional":true}],"status":"live","first_seen_at":"2026-09-15T10:23:30Z","employer_posted_date":"2026-09-15","last_verified_at":"2026-11-06T17:48:42Z","board_verified":true,"closed_at":null,"days_open":52,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":52},"description":"We are building the AI layer of the Radio Access Network, applying agentic AI, machine learning, and LLMs to real RAN challenges - from network operations and root cause analysis to PHY simulation and uplink link adaptation.\nWe are looking for a Technical Lead to own the end-to-end architecture and technical direction of these AI systems. This is a hands-on senior individual contributor role, combining architecture, coding, technical leadership, and mentorship.\nYou will work from our Kfar Saba site alongside RAN, PHY, and software teams, building production-grade AI solutions for real cellular networks.\nWhat you'll do:\nDefine the end-to-end architecture for AI/ML systems, including agent orchestration, model serving, data pipelines, RAG, and evaluation infrastructure.\nDrive technical decisions around models, frameworks, deployment strategies, and build-vs-buy approaches.\nPrototype and implement critical components while setting engineering and code-quality standards.\nLead AI solutions from prototype to production, including CI/CD, monitoring, model lifecycle, versioning, and rollback.\nEstablish evaluation frameworks, benchmarks, and safety criteria for AI systems operating on network data.\nMentor engineers through architecture discussions, design reviews, and code reviews.\nCollaborate with RAN Systems, PHY, L2/L3, Product, and customer-facing teams to translate network challenges into practical AI/ML solutions.\nContribute to technical roadmap discussions and customer-facing architecture discussions.\nWhat you should have:\n7+ years of experience in software or ML engineering, with significant experience delivering production systems.\nProven technical leadership and experience owning the architecture of complex systems end to end.\nStrong Python skills and hands-on experience with PyTorch or similar ML frameworks.\nPractical experience with LLM-based systems, including agents, tool calling, RAG, orchestration, prompt/context engineering, and evaluation.\nStrong understanding of classical ML, including time-series analysis, anomaly detection, and supervised learning.\nWorking knowledge of 4G/5G RAN architecture, L1/L2/L3, network KPIs, and cellular network operations.\nExperience with MLOps, containers, CI/CD, experiment tracking, model monitoring, and production deployment.\nExcellent English and strong technical communication skills.\nNice to have:\nHands-on experience in RAN, wireless infrastructure, telecom operators, or chipset companies.\nKnowledge of O-RAN, RIC, rApps/xApps, and E2/A1/O1 interfaces.\nExperience with link adaptation, scheduling, RRM, channel modeling, or PHY simulation.\nExperience with reinforcement learning or contextual bandits for real-world control problems.\nExperience deploying ML models in real-time or resource-constrained environments.\nBackground in signal processing, communications, or information theory.\nM.Sc. / Ph.D. in Computer Science, Electrical Engineering, Applied Mathematics, or a related field.","description_format":"text","description_chars":2973,"description_truncated":false,"requirements":{"experience_years_min":7,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"master","optional":false},"security_clearance":false,"languages":[{"language":"English","level":"All 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