Parallel Wireless

Parallel Wireless

Senior/Principal Local LLM & Generative AI Platform Engineer

US · Principal

Sponsorship not specifiedDetected 43 days ago
PythonJavaGoC++CGitVector DatabasesDockerKubernetesCI/CDLinuxPlatform EngineeringMachine LearningData EngineeringData ScienceNLPLLMsRAGMLOpsCybersecurityIncident ResponseElectrical EngineeringTest Automation5G/LTE

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About the role

  • The successful candidate will understand that a useful enterprise LLM is more than a model and a chat interface.
  • It requires trustworthy source grounding, strong access controls, repeatable evaluation, careful tool permissions, observable production services, and an operating model that keeps data, indexes, prompts, models, and dependencies current.

Responsibilities

  • Own the architecture and technical roadmap for a secure, reliable, and maintainable local LLM platform deployed in Parallel Wireless-controlled infrastructure.
  • Partner with engineering, product, support, IT, information security, legal, and domain experts to prioritize high-value use cases and translate them into measurable product and platform requirements.
  • Build a modular inference and model-gateway layer with stable APIs, model routing, streaming, concurrency controls, quotas, and the ability to change models or serving backends without rewriting every application.
  • document model provenance, licenses, limitations, security posture, hardware needs, and total cost of ownership.
  • Optimize serving across available CPU, GPU, and accelerator resources using techniques such as continuous batching, caching, parallelism, quantization, and right-sized context limits while protecting output quality.
  • Create release gates and reproducible regression tests for changes to models, prompts, tools, embeddings, retrieval logic, indexes, and serving configurations
  • support canary releases, rollback, and clear approval paths.
  • Implement end-to-end observability for model and agent workflows, including traces, errors, time to first token, inter-token latency, throughput, queue time, resource utilization, saturation, availability, and user feedback.
  • Design safe tool-calling and agent workflows with least-privilege access, sandboxing, input and output validation, bounded execution, human approval for consequential actions, and complete traceability.
  • Evaluate open-weight language, code, embedding, reranking, and, where useful, multimodal models against PW-specific tasks; document model provenance, licenses, limitations, security posture, hardware needs, and total cost of ownership.

Requirements

  • BSc or MSc in Computer Science, Computer Engineering, Electrical Engineering, Data Science, or a related field, or equivalent practical experience.
  • Typically 7+ years of hands-on experience in production software, ML platform, search, data, or infrastructure engineering, including meaningful recent experience shipping LLM-powered systems
  • Strong Python engineering skills and experience designing maintainable APIs, services, libraries, and data pipelines.
  • Experience with Go, Java, or C/C++ is an advantage.
  • Experience defining task-specific LLM evaluations using representative datasets, strong baselines, domain-expert review, automated metrics, human feedback, error analysis, and regression thresholds.
  • Experience deploying and operating containerized services on Linux using Docker and Kubernetes or an equivalent orchestration environment.
  • Practical experience with GPU-backed model serving, performance profiling, capacity planning, monitoring, and reliability engineering.
  • Strong knowledge of distributed-system fundamentals, authentication and authorization, API security, secrets handling, encryption, auditability, and data lifecycle controls.
  • Experience with Git, automated testing, CI/CD, infrastructure as code, observability, and production incident response.
  • exceptional candidates with equivalent depth are welcome.
  • Strong Python engineering skills and experience designing maintainable APIs, services, libraries, and data pipelines. Experience with Go, Java, or C/C++ is an advantage.
  • Strong understanding of transformer-based language models and production inference, including tokenization, context management, batching, KV caching, parallelism, quantization, structured output, tool calling, and common model failure modes.
  • Demonstrated experience building production RAG or enterprise-search systems using embeddings, vector and/or lexical search, metadata filtering, reranking, source attribution, and systematic retrieval evaluation.
  • Sound technical judgment about quality, security, maintainability, hardware efficiency, and total cost-not just model benchmark scores.
  • Ability to lead an ambiguous, cross-functional initiative, explain complex AI behavior in plain language, and help other teams ship safely on a shared platform.

Nice to have

  • Experience operating LLMs in on-premises, private-cloud, restricted-network, or air-gapped environments.
  • Hands-on experience with current inference runtimes and serving systems such as vLLM, SGLang, TensorRT-LLM, llama.cpp, Ray Serve, KServe, Triton, or equivalent technologies.
  • Experience optimizing inference on NVIDIA and/or AMD GPUs using CUDA, ROCm, profiling tools, tensor parallelism, pipeline parallelism, speculative decoding, prefix/KV caching, or related techniques.
  • Experience with parameter-efficient fine-tuning methods such as LoRA/QLoRA, dataset curation, synthetic-data generation, distillation, and post-training evaluation.
  • Experience red-teaming LLM or agent systems for prompt injection, sensitive-data disclosure, poisoned retrieval content, excessive agency, and insecure output handling.
  • Experience working across heterogeneous compute platforms and making performance, energy, and TCO tradeoffs for enterprise AI workloads.
  • Contributions to relevant open-source AI, search, MLOps, or infrastructure projects.

Skills

  • Parallel Wireless is a U.S.-based pioneer in Open RAN innovation, transforming how mobile networks are built, optimized, and powered.
  • You will turn promising prototypes into a dependable internal capability:
  • The platform should be extensible to additional approved use cases as needs and model capabilities evolve.

Company info

  • Our software-centric, hardware-agnostic approach brings intelligence into the RAN while helping customers reduce complexity and total cost of ownership.
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This listing is sourced directly from Parallel Wireless's careers page and normalized into a canonical job model.