5 дней назад
Lead AI Backend Engineer
100 000 - 180 000CAD
Мэтч & Сопровод
Для мэтча с этой вакансией нужен Plus
Описание вакансии
Текст:
TL;DR
Lead AI Backend Engineer (AI/LLM): Building an LLM-agnostic, agent-driven platform and high-throughput backend APIs for millions of users with an accent on multi-agent orchestration, RAG, reliability, security, and cost efficiency. Focus on designing distributed systems, integrating multiple model providers, optimizing latency and FinOps, and leading engineering teams.
Location: Remote in Canada
Salary: CAD 100,000–180,000 annually
Company
helps businesses establish and protect their digital presence through domain, brand, website, and security services.
What you will do
- Set the technical architecture for an LLM-agnostic, agent-driven platform.
- Design and deliver high-throughput APIs with .NET, C#, Python, FastAPI, SQLModel, and Semantic Kernel.
- Lead multi-agent orchestration, tool calling, structured outputs, and multi-step workflows across multiple model providers.
- Guide production RAG systems using Azure AI Search, pgvector, Chroma, and equivalent retrieval platforms.
- Own CI/CD pipelines, observability, security controls, evaluation, and FinOps for LLM workloads.
- Partner with Product and Security while mentoring and hiring engineers.
Requirements
- 7+ years of experience building and scaling production backend systems, including 2+ years in a technical leadership role.
- Expertise in REST API design with Python FastAPI or .NET C# APIs, dependency injection, middleware, profiling, performance optimization, and distributed systems.
- Production experience with Semantic Kernel or equivalent agent and LLM frameworks.
- Experience delivering a production RAG system with measurable latency and quality KPIs.
- Deep PostgreSQL expertise with SQLModel, SQLAlchemy 2, and Alembic migrations at scale.
- Fluency with CI/CD, Docker, infrastructure as code, and blue-green or canary deployment strategies.
Nice to have
- Message queues and event-driven architectures using RabbitMQ, Kafka, or Azure Service Bus.
- GPU inference fleets, self-hosted open-weight models, or serverless model hosting.
- Model gateways, intelligent routing, MCP, prompt caching, and LLM cost optimization.
- Automated agent evaluations, LLM-as-judge techniques, safety guardrails, and context engineering.
Culture & Benefits
- Work-life balance and a culture focused on diversity, inclusion, and belonging.
- Learning, development, mentoring, and career growth opportunities.
- Health benefits, vacation policy, punctuality bonus, and family-oriented support.
- Employee affinity groups and opportunities to connect with colleagues globally.
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