AI Engineer
CodeRound is hiring for this role for a VC-backed startup.
- AI Engineer
- AI/ML
- RAG
- Retrieval Systems
- Retrieval Quality
- Chunking Strategy
- Embedding Models
- Re-ranking
- AI Agents
- Agentic AI
- Multi-Agent Systems
- Agent Orchestration
- LangGraph
- Tool Calling
- Chatbots
- Evaluation Frameworks
- AI Evaluation
- LLM
- Anthropic
- OpenAI
- Vector Databases
- Vector DBs
- AI Tooling
- Agent Libraries
- Fine-tuning
- RLHF
- Distillation
- System Design
- AI Backend
- Backend
- Enterprise SaaS
- B2B SaaS
- Sales Tech
- Production AI
- AI/ML Systems
- AI Quality
- Model Migration
- CS
We’re looking for an AI Engineer to build and own production-grade AI systems, including RAG pipelines, AI agents, production chatbots, and evaluation frameworks. You’ll work with frontier models, vector databases, and LangGraph to improve retrieval quality, agent reliability, and overall AI performance. The ideal candidate has 2–3+ years of hands-on production AI/ML experience, strong system design skills, and deep expertise in RAG and AI evaluation. This is a high-ownership role where you’ll be expected to ship, measure, iterate, and make meaningful improvements to AI quality every week.
What you'll do
- Own the intelligence layer
- Build RAG and retrieval systems that pull in data from multiple sources to make every AI output relevant and grounded in real context
- Build AI agents and workflows — research agents, enrichment agents, follow-up sequencing agents that operate reliably at scale without human intervention
- Build the evaluation framework that measures output quality before anything reaches real prospects
- Define what "good" means and build the infrastructure to measure it consistently
- Make the AI meaningfully better, measurably, every week
- Make measurable improvements to retrieval quality or agent reliability
- Diagnose the biggest AI quality gaps and create a prioritized plan to close them
- Stay current with new model releases, new frameworks, and new research
- Evaluate and make decisions on the right retrieval strategy, the right eval metric, the right model for a given task
- Work on backend or frontend that work with the AI backend when required
Must have
3+ years of hands-on experience shipping AI/ML systems in production — not demos, not prototypes Deep experience building and owning RAG pipelines — retrieval quality, chunking strategy, embedding models, re-ranking Experience building chatbots — one that runs in production Experience building evaluation frameworks and measuring whether AI output is good before it reaches users Strong, current knowledge of the AI tooling landscape — models, frameworks, vector DBs, agent libraries You have a system, version control, and a way of measuring what works Good system design skills — AI backend and backend in general Good idea of how things work end to end in an enterprise SAAS world Clear and unambiguous communication skills Ability to break down a problem into smaller logical sub problems Must think from user experience and work backward in terms of what it means to implement
Good to have
Experience building multi-agent systems that run reliably at scale Familiarity with fine-tuning, RLHF, or distillation Background in sales tech or B2B SaaS context Experience having migrated a production AI system from one model or framework to another without breaking things CS background is strongly preferred Judgment on what is good user experience