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RAG-powered knowledge systems
Build retrieval systems that answer from internal docs, support tickets, product data, or workflows using reliable grounding.
Role overview
What makes an Eventum LLM Engineer special?
Our LLM Engineers have a fundamental understanding of how LLMs work both in theory and in the real world. This makes them able to plan ahead and around their intrinsic capabilities to deliver systems reliable enough to use in production, not closed demos.
They've proved they can design retrieval systems, build evaluation harnesses, debug prompt and model behavior, tune models when needed, connect LLMs to tools and workflows, figure out token economy scales and trade-offs, and overall help teams understand exactly where the system is (or likely will) fail.
Unlike a generic ML engineer or prompt specialist, a strong LLM engineer works systematically across data context, model behavior, safety and issue reproducibility, product constraints, and production quality.
Typical use cases
Typical use cases002
LLM evaluation & reliability systems
Design eval harnesses, regression tests, and monitoring workflows to catch hallucinations, quality regressions, and brittle behavior.
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Domain-specific fine-tuning
Fine-tune or post-train open-weight models on proprietary data for specialized use cases in domains like healthcare, legal, finance, or enterprise workflows.
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Tool use & agent behavior
Build LLM systems that call tools, follow process logic, handle failures, and expose the right controls to users.
Key skills
Fine-tuning: LoRA, QLoRA, full fine-tuning, post-training workflows
RAG architecture: chunking strategies, embedding models, vector stores, hybrid search, reranking
Evaluation harnesses: LLM-as-judge, RAG evals, rubric-based testing, regression monitoring
Prompt engineering and prompt optimization at scale
Inference optimization: vLLM, quantization, batching, serving tradeoffs
LangChain, LlamaIndex, or comparable orchestration frameworks
API integration across OpenAI, Anthropic, Cohere, and open-weight models
Production thinking around latency, cost, observability, and failure handling
Testimonials
Trusted by teams who ship AI to production.
Founders, CTOs and product leads on what changed after Eventum matched them with the right AI specialist.
“Eventum helped us go from stuck to cutting edge in a matter of months, rewriting our entire ML training stack and continually supporting our R&D efforts.”
Kevin JacobsVP Data Science, Deepcell“Eventum came in and quenched our MLOps fire in quick order ensuring our ML Scientists could make rapid progress. We then immediately hired them to help build us a generative AI audio model from scratch”
Shawn ZhangCTO / Founder, Sanas"Eventum built and managed our ML team, models, and software from the ground up at below market rates delivering incredible results. They are an essential partner for us that I couldn’t recommend more highly."
Jim BenedettoCAO, PLAI Labs
Why hire through Eventum
Why hire through Eventum
01
Production-tested, not lab-tested
We filter for engineers who have shipped LLM systems in real environments — with latency, evals, incident response, and cost control under traffic.
02
Role-specific vetting
Our screening includes LLM-relevant work: retrieval design, eval harnesses, debugging model behavior, and production tradeoffs — not a generic coding interview.
03
Fast shortlist, senior signal
You get a focused shortlist built around the actual role, not broad recruiter noise.
Oleksandr K.
Senior LLM Engineer · 9 yrsLed RAG, evaluation, and LLM optimization work for healthcare and enterprise SaaS products. Strong across retrieval architecture, eval harnesses, prompt/model behavior, and production LLM reliability.
PyTorchLangChainRAGEvalsAWS
Results
Reduced hallucination rate by 34% through a production GPT-4 evaluation pipeline.
Previously Worked at:
Alex M.
Senior LLM Engineer · 11 yrsSpecializes in fine-tuning and self-hosted inference for high-volume products: post-training (LoRA, SFT, DPO), quantization, and serving.
PyTorchvLLMTritonLoRA/DPORay
Results
Fine-tuned an open-weight model to GPT-4-level quality on a support-automation workflow at ~1/8 the inference cost.
Previously Worked at:
From voice calls to answers: RAG on your own data.
Nouns DAO's decisions lived in hours of voice calls and fast-moving chat threads that nobody could search. Eventum engineered the full pipeline behind Roko, their governance assistant: automatic transcription and summarization, auto-indexing of voice and chat into a central knowledge base, and a RAG chatbot in Discord that answers from what was actually said. The visible part is a chatbot. The engineering that makes it trustworthy is the retrieval layer underneath.
- Voice and chat auto-indexed into one knowledge base
- Grounded answers via RAG, inside Discord
- Web app for search and audio uploads
Related roles
Other roles often hired alongside LLM Engineers
Related case studies
