Job VC

AI Systems Engineer (LLM / Agents / RAG)
Remote | Full-time | Flexible Schedule
Our client is looking for a strong AI Systems Engineer to help design and build production-grade AI infrastructure, agent-based systems, and workflow automation.
You’ll work directly with the Head of AI on building the company’s internal AI ecosystem: multi-agent systems, RAG pipelines, AI-powered analytics, automation workflows, integrations, and operational AI tooling.
This role is ideal for someone who enjoys building real AI systems end-to-end — not just experimenting with prompts, but designing scalable architectures and shipping production-ready solutions.
Responsibilities
Design and develop production AI/LLM systems
Build agent-based workflows and multi-agent orchestration systems with memory, routing, and tool usage
Develop end-to-end RAG pipelines and knowledge base systems:
ingestion
chunking
embeddings
retrieval
vector databases
citation tracing
hallucination mitigation
Build AI-powered workflow automation for marketing, analytics, operations, and reporting
Integrate LLM systems with CRMs, APIs, external platforms, and internal tools
Develop integrations using REST APIs, webhooks, and third-party services
Participate in building unified analytics and attribution systems across multiple channels and platforms
Support creative/content AI pipelines (image/video generation workflows)
Improve reliability, observability, latency, and cost-efficiency of AI systems
Deploy and maintain AI systems in production environments
Work closely with the Head of AI on architecture and AI ecosystem development
Requirements
2+ years of hands-on experience building production AI/LLM systems (not educational or pet projects)
Strong Python skills
Experience building production backend systems and integrations
Practical experience with:
agent orchestration frameworks (LangGraph-class frameworks)
tool/function calling
structured outputs
guardrails
multi-agent systems
Strong understanding of RAG systems and retrieval pipelines
Experience with vector databases:
Pinecone
Qdrant
pgvector
Experience with PostgreSQL and data-layer architecture
Understanding of:
embeddings
chunking strategies
retrieval quality
citation tracing
hallucination mitigation
Experience with workflow automation tools:
n8n
Make
Zapier
or similar
Basic LLMOps understanding:
evals
observability (Langfuse-class tooling)
latency optimization
cost optimization
fallback model chains
Experience working with OpenAI and/or Anthropic Claude APIs
Understanding of MCP (Model Context Protocol)
Experience with REST APIs, webhooks, and external integrations
English level: B1+
Nice to Have
Experience with AI automation for marketing, analytics, or operations
Experience with e-commerce ecosystems:
Shopify
Amazon
TikTok Shop
marketplace analytics
Experience with attribution or marketing analytics systems
Experience with image/video generation workflows or ComfyUI
Understanding of privacy/compliance concepts (CCPA-class requirements)
Portfolio or GitHub with production AI projects
What Matters in This Role
Strong ownership mindset
Ability to translate business problems into working AI architectures
Production-oriented engineering mindset
Ability to work independently in async environments
Focus on measurable and reliable AI systems rather than experimental demos
We Offer
Fully remote work from anywhere
Flexible schedule
Direct collaboration with the Head of AI
Modern AI stack and real production AI challenges
High ownership and technical freedom
Paid vacation and sick days