Senior/Principal Local LLM & Generative AI Platform Engineer
Posted 14 days ago · 0 applicants
Saving, applying or scoring takes a few seconds to set up your free account.
The role in plain words
This role involves building and operating a secure, local generative AI and LLM platform for internal engineering and business teams. Day-to-day responsibilities include selecting and optimizing open-weight models, designing permission-aware retrieval-augmented generation (RAG) pipelines, creating reusable APIs, and integrating LLMs into existing technical workflows. You will also establish production operations, monitoring latency, quality, security, and capacity for various enterprise use cases.
- Applied LLM engineering and platform architecture
- Building permission-aware retrieval-augmented generation (RAG) and search pipelines
- Selecting, optimizing, and deploying open-weight models locally
- Creating reusable APIs, tools, and integrating with existing engineering workflows
- Establishing evaluation frameworks for LLM quality, safety, latency, and capacity
Extracted from the job description · kept up to date automatically
Who this suits
This role suits experienced platform and AI engineers with strong backgrounds in applied LLM architecture, security controls, search/data pipelines, and production operations. It is less ideal for candidates looking for purely research-focused positions or those without experience in building secure enterprise infrastructure.
Full job description
Original listing · kept for referenceParallel 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.
Questions about this role
- This listing did not state a salary. We only show pay when the employer publishes it.
- Applied LLM engineering and platform architecture, Building permission-aware retrieval-augmented generation (RAG) and search pipelines, Selecting, optimizing, and deploying open-weight models locally, Creating reusable APIs, tools, and integrating with existing engineering workflows, Establishing evaluation frameworks for LLM quality, safety, latency, and capacity