Senior/Principal Local LLM & Generative AI Platform Engineer

🔥 14 hours ago

🇺🇸 United States – Remote

⏰ Full Time

🟠 Senior

🏗️ Platform Engineer

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👻 Ghost score 11%

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Parallel Wireless

WebsiteLinkedInAll Job Openings

501 - 1000 employees

Founded 2012

📡 Telecommunications

🔧 Hardware

Telecommunications • Hardware

Parallel Wireless is a technology company that pioneers innovative wireless infrastructure solutions through its OpenRAN technology. The company focuses on providing cost-effective and flexible wireless networks covering all generations from 2G to 5G. Parallel Wireless aims to facilitate mobile operators by delivering coverage in urban, suburban, and rural areas while enhancing capacity and performance using cloud-native and software-driven approaches. Through their OpenRAN technology, they are able to deploy macro, massive MIMO, and small cell networks, offering advanced 5G capabilities and supporting easy technology migration and upgrades. Known for reducing complexities and operational costs, Parallel Wireless enables dynamic and automated network management and analytics. Their mission involves driving modern wireless solutions that ensure widespread and equal access to advanced connectivity worldwide.

📋 Description

• 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 interchangeable models or serving backends • Evaluate open-weight language, code, embedding, reranking, and multimodal models against company-specific tasks; document provenance, licenses, limitations, security posture, hardware needs, and total cost of ownership • Optimize serving across CPU, GPU, and accelerator resources using continuous batching, caching, parallelism, quantization, and right-sized context limits • Design and operate RAG and enterprise-search pipelines for approved repositories, wikis, tickets, standards, design documents, test results, logs, and support content • Enforce source-system permissions throughout ingestion and retrieval; integrate identity, SSO, RBAC, secrets management, and audit logging • Establish versioned evaluation datasets and automated offline and online evaluation for retrieval quality, groundedness, factual accuracy, citation quality, code correctness, task completion, latency, safety, and refusal behavior • Create release gates and reproducible regression tests; support canary releases, rollback, and approval paths • Implement end-to-end observability for model and agent workflows, including traces, errors, latency, throughput, queue time, resource utilization, saturation, availability, and user feedback • Design safe tool-calling and agent workflows with least-privilege access, sandboxing, validation, bounded execution, human approval, and traceability • Integrate the platform with developer environments, source-control and CI workflows, knowledge systems, ticketing systems, and internal applications through SDKs, APIs, and reference implementations • Build production foundations including CI/CD, configuration and model registries, backups, disaster recovery, capacity planning, vulnerability management, incident response, and lifecycle policies • Protect proprietary and personal information through network isolation, encryption, retention controls, redaction, secure logging, and defenses against prompt injection, data poisoning, unsafe output handling, and model-supply-chain risks • Determine when prompting or retrieval improvements suffice and when fine-tuning, distillation, or other adaptation is justified • Enable adoption through documentation, examples, training, office hours, telemetry, and structured feedback • Communicate architecture decisions, quality evidence, risk, capacity, and roadmap tradeoffs to technical and business stakeholders

🎯 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; exceptional candidates with equivalent depth are welcome • Strong Python engineering skills and experience designing maintainable APIs, services, libraries, and data pipelines • 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 • 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 • Experience with Go, Java, or C/C++ is an advantage • Experience operating LLMs in on-premises, private-cloud, restricted-network, or air-gapped environments is nice to have • Experience with inference runtimes and serving systems such as vLLM, SGLang, TensorRT-LLM, llama.cpp, Ray Serve, KServe, or Triton is nice to have • 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 is nice to have • Experience with model and experiment registries, LLM tracing and evaluation platforms, vector databases, hybrid-search engines, and production data-orchestration frameworks is nice to have • Experience with LoRA/QLoRA, dataset curation, synthetic-data generation, distillation, and post-training evaluation is nice to have • Experience building code intelligence, repository-aware assistants, developer tools, or IDE and CI integrations for large C/C++ and Python codebases is nice to have • Familiarity with Active Directory or another enterprise identity provider, fine-grained document authorization, data-loss prevention, secure software supply chains, model licensing, and AI governance is nice to have • Experience red-teaming LLM or agent systems is nice to have • Knowledge of telecommunications, 3GPP, RAN/Open RAN, cloud-native network functions, or technical-support workflows is nice to have • Contributions to relevant open-source AI, search, MLOps, or infrastructure projects are nice to have