Senior/Principal Local LLM & Generative AI Platform Engineer

Responsibilities

• 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.
• 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.

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 understanding of transformer-based language models

What you bring

Strong Python engineering skills and experience designing maintainable APIs, services, libraries, and data pipelines. Experience with Go, Java, or C/C++ is an advantage.