Senior/Principal Local LLM & Generative AI Platform Engineer

Job description

Parallel Wireless is a U.S.-based pioneer in Open RAN innovation, transforming how mobile networks are built, optimized, and powered. Through our GreenRAN™ portfolio, we help operators deliver secure, energy-efficient, automated, and flexible connectivity across 2G, 3G, 4G, 5G, and the path toward 6G. Our software-centric, hardware-agnostic approach brings intelligence into the RAN while helping customers reduce complexity and total cost of ownership. Parallel Wireless is looking for a hands-on technical leader to build and operate a secure local large-language-model platform for the company. The platform will allow engineering and business teams to use generative AI with proprietary source code, product documentation, technical standards, test artifacts, support knowledge, and other approved internal data while keeping sensitive information within company-controlled environments. This is a senior individual-contributor role spanning applied LLM engineering, platform architecture, search and data pipelines, security, and production operations. You will turn promising prototypes into a dependable internal capability: selecting and optimizing open-weight models, building permission-aware retrieval, creating reusable APIs and tools, integrating with existing engineering workflows, and establishing objective ways to measure quality, safety, latency, capacity, and business value. 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. You will make pragmatic build-versus-buy decisions and choose the simplest approach—search, retrieval-augmented generation (RAG), prompting, workflow automation, or model adaptation—that meets each use case. Initial use cases may include engineering knowledge discovery, source-code understanding, troubleshooting assistance, technical-document Q&A and summarization, test and log analysis, and drafting structured engineering artifacts. The platform should be extensible to additional approved use cases as needs and model capabilities evolve. ### What you will do: - 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. - 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. - 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. - Design and operate RAG and enterprise-search pipelines for approved repositories, wikis, tickets, standards, design documents, test results, logs, and support content, including parsing, chunking, metadata, embeddings, hybrid retrieval, reranking, freshness, citations, and deletion. - Enforce source-system permissions throughout ingestion and retrieval so that the platform never exposes content a user is not authorized to access; integrate with company identity, SSO, role-based access control, 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 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. - Integrate the platform into the tools employees already use—such as developer environments, source-control and CI workflows, knowledge systems, ticketing systems, and internal applications—through reusable SDKs, APIs, and reference implementations. - Build the operational foundations for production use: CI/CD, configuration and model registries, backups, disaster recovery, capacity planning, dependency and vulnerability management, incident response, and lifecycle policies for models and data. - Protect proprietary and personal information through network isolation, encryption, retention controls, redaction where appropriate, secure logging, and defenses against prompt injection, data poisoning, unsafe output handling, and model-supply-chain risks. - Determine when prompt or retrieval improvements are sufficient and when parameter-efficient fine-tuning, distillation, or other adaptation is justified by measured quality gains. - Make the platform usable beyond the core AI team through documentation, examples, training, office hours, and hands-on collaboration; use telemetry and structured feedback to improve adoption and effectiveness. - Communicate architecture decisions, quality evidence, risk, capacity, and roadmap tradeoffs clearly to technical and business stakeholders. ### What you bring: - 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. Experience with Go, Java, or C/C++ is an advantage. - Strong understanding of transformer-based language models