Principal AI Engineer
Role Overview
Hands-on Principal AI Engineer role leading architecture, development, and optimization of advanced AI systems and scalable backend platforms.
The position is an individual-contributor role, not a traditional people-management role, with active coding and engineering alongside technical leadership.
Key Skills for This Role
Full Job Posting
Role overview
Hands-on Principal AI Engineer role leading architecture, development, and optimization of advanced AI systems and scalable backend platforms.
The position is an individual-contributor role, not a traditional people-management role, with active coding and engineering alongside technical leadership.
Key responsibilities
- Define technical architecture for scalable AI platforms and distributed backend systems.
- Design and optimize LLM inference and serving infrastructure using vLLM, TGI, or TensorRT-LLM.
- Build production-grade RAG pipelines, retrieval systems, vector search, reranking, and AI agent solutions.
- Develop scalable backend services, APIs, microservices, and event-driven architectures.
- Optimize PostgreSQL databases, cloud infrastructure, and Kubernetes-based deployments.
- Implement AI evaluation frameworks, regression testing, monitoring, and observability.
- Improve system performance, scalability, reliability, latency, and cost efficiency.
- Lead technical design reviews and resolve complex engineering challenges.
- Mentor senior and staff engineers and establish engineering best practices.
- Evaluate AI models, frameworks, and infrastructure technologies to guide technical decisions.
Required qualifications
- At least 8 years of relevant software engineering and AI experience, with technical leadership at Staff, Principal, or equivalent level.
- Strong hands-on proficiency in Python and its production ecosystem.
- Working knowledge of at least one additional language: Go, Rust, Java, or Kotlin.
- Production AI system design and deployment experience using LLMs, RAG, retrieval, or AI agents.
- Strong understanding of model serving, embeddings, vector databases, and AI observability.
- Experience with distributed systems, microservices, APIs, and event-driven architecture.
- Strong knowledge of PostgreSQL, database optimization, and system reliability.
- Experience with Microsoft Azure, Docker, Kubernetes, and Terraform or equivalent infrastructure automation tools.
- Understanding of scalability, consistency, idempotency, retries, back-pressure, and service-level objectives.
- Ability to lead architecture decisions, solve complex technical problems, and mentor engineering teams.
- Experience using AI-assisted development tools and coding agents.
Preferred skills and experience
- Preferred agent frameworks include LangGraph, LangChain, AutoGen, Microsoft Agent Framework, or OpenAI Agent SDK.
- Preferred inference and vector tools include vLLM, TGI, TensorRT-LLM, pgvector, or Qdrant.
- Preferred technologies include Kafka, Azure Service Bus, Azure Event Hubs, Redis, and Azure Blob Storage.
- Experience with Langfuse, Grafana, Prometheus, or similar observability tools is preferred.
- Arabic-language AI, voice AI, conversational AI, and AI delivery in regulated or mission-critical environments are preferred.
Apply for this job in 1 click
Skip the repetitive application forms
Install the Base Career Chrome Extension and autofill job applications across major job boards with your profile.
Trusted by over 500,000 job seekers on Base Career