Senior/Principal Local LLM & Generative AI Platform Engineer

🕒 Setembro 9

🇺🇸 Estados Unidos – Remoto (EUA)

⏰ Tempo Integral

🟠 Sênior

🏗️ Engenheiro de Plataforma

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Parallel Wireless

SiteLinkedInTodas as Vagas

501 - 1000 funcionários

Fundada em 2012

📡 Telecomunicações

🔧 Hardware

Telecommunications • Hardware

A Parallel Wireless é uma empresa de tecnologia que inova em soluções de infraestrutura sem fio por meio de sua tecnologia OpenRAN. A empresa se concentra em fornecer redes sem fio econômicas e flexíveis cobrindo todas as gerações de 2G a 5G. A Parallel Wireless visa facilitar os operadores móveis, oferecendo cobertura em áreas urbanas, suburbanas e rurais, ao mesmo tempo que melhora a capacidade e o desempenho utilizando abordagens nativas da nuvem e orientadas por software. Por meio da tecnologia OpenRAN, eles podem implantar redes macro, massive MIMO e small cell, oferecendo capacidades avançadas de 5G e suportando fácil migração e atualizações tecnológicas. Conhecida por reduzir complexidades e custos operacionais, a Parallel Wireless possibilita a gestão dinâmica e automatizada de redes e análises. Sua missão envolve impulsionar soluções sem fio modernas que assegurem amplo e igual acesso à conectividade avançada em todo o mundo.

Descrição

• 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

🎯 Requisitos

• 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

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