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Senior/Principal Local LLM & Generative AI Platform Engineer

1 month ago
United States
Full-time
Senior
AI Engineer
DevOps and Infrastructure
Active Directory C C++ CI/CD Docker Encryption Generative AI Git Go Java Kubernetes Linux Python Secrets Management
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Parallel Wireless

Parallel Wireless

Parallel Wireless leads the OpenRAN movement with innovative cloud-native architecture, enabling cost-effective cellular network deployment globally.

Wireless Telecommunication Services
251-1K
Founded 2012
$2M raised
View All Jobs 15

Description

  • Own the architecture and technical roadmap for a secure, reliable local LLM platform.
  • Partner with engineering, product, support, IT, security, legal, and domain experts to prioritize use cases and define measurable requirements.
  • Build modular inference and model-gateway services with stable APIs, model routing, streaming, concurrency controls, and quotas.
  • Evaluate, select, and optimize open-weight language, code, embedding, reranking, and multimodal models.
  • Design and operate RAG and enterprise-search pipelines with metadata, embeddings, hybrid retrieval, reranking, citations, freshness, and deletion controls.
  • Enforce permissions across ingestion and retrieval, integrating identity, SSO, RBAC, secrets management, and audit logging.
  • Establish automated evaluation, regression testing, release gates, canary deployments, rollback procedures, and quality metrics.
  • Implement observability, production operations, CI/CD, registries, backups, disaster recovery, capacity planning, and incident response.
  • Design secure tool-calling and agent workflows with least privilege, sandboxing, validation, bounded execution, and human approval.
  • Integrate the platform into developer, source-control, CI, knowledge, ticketing, and internal application workflows through APIs and SDKs.

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 software, ML platform, search, data, or infrastructure engineering, including recent LLM production experience.
  • Strong Python skills and experience building maintainable APIs, services, libraries, and data pipelines; Go, Java, or C/C++ experience is advantageous.
  • Strong understanding of transformer models and production inference, including tokenization, context management, batching, KV caching, parallelism, quantization, structured output, and tool calling.
  • Production experience building RAG or enterprise-search systems using embeddings, vector or lexical search, metadata filtering, reranking, source attribution, and retrieval evaluation.
  • Experience defining task-specific LLM evaluations using representative datasets, automated metrics, human feedback, error analysis, and regression thresholds.
  • Experience deploying containerized Linux services with Docker and Kubernetes or equivalent orchestration.
  • Experience with GPU-backed model serving, performance profiling, capacity planning, monitoring, reliability engineering, Git, testing, CI/CD, infrastructure as code, and incident response.
  • Strong knowledge of distributed systems, authentication, authorization, API security, secrets, encryption, auditability, and data lifecycle controls.
  • Preferred experience with private or air-gapped deployments, inference systems such as vLLM or TensorRT-LLM, GPU optimization, fine-tuning, vector databases, AI governance, LLM red-teaming, telecommunications/Open RAN, or relevant open-source projects.

Interested in this position?

Apply directly on the company website

Apply Now

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