001
GenAI product development
Build customer-facing or internal AI products with real workflows, usable interfaces, and production-ready architecture.
Role overview
What we expected from AI Application Engineers at Eventum
AI Application Engineers that clear our vetting process have demonstrated the ability to turn AI capability into usable software. They connect LLMs, retrieval systems, tools, APIs, product workflows, and user interfaces into applications that people can actually use.
Our take on an AI Application Engineer is that unlike a pure model specialist, these individuals are responsible for the full product layer around AI: backend orchestration, frontend UX, agent workflows, integrations, authentication, evaluation hooks, error handling, and production deployment.
This is the role teams need when the challenge isn't just model output quality, but getting an AI product or workflow working efficiently into the hands of users.
Typical use cases
Typical use cases002
Agentic workflow automation
Design agent systems with tool use, task orchestration, state management, observability, and human review where needed.
003
LLM integrations and tool use
Integrate OpenAI, Anthropic, open-weight models, retrieval systems, APIs, databases, and internal tools into working applications.
004
Internal copilots and workflow tools
Build copilots, assistants, review tools, search interfaces, and workflow automations that fit how teams actually work.
Key skills
Full-stack product engineering: React, Next.js, TypeScript, Python, FastAPI, Node.js, API design, authentication, and production web application patterns.
LLM and GenAI integration: OpenAI, Anthropic, open-weight models, model routing, prompt/model behavior, tool use, and structured outputs.
Agent workflow design: Function calling, tool orchestration, state management, retries, human-in-the-loop review, and failure handling.
RAG and knowledge integration: Retrieval pipelines, vector search, document ingestion, metadata, grounding, and integration with internal knowledge systems.
Evaluation and observability: Evals, traces, logs, regression tests, feedback loops, monitoring, and product-quality checks for AI behavior.
Production deployment: Cloud deployment, CI/CD, scaling, latency, rate limits, security, data handling, and operational readiness.
Product and UX judgment: Designing AI workflows that users understand, trust, and can control.
Communication and ownership: Working with product, design, engineering, and leadership to turn ambiguous AI goals into shipped functionality.
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 matched us with an LLM engineer in six days. Two sprints later our support copilot was live in production.”
Maya LindqvistVP of Engineering, Nordflow“The vetting is real. The first candidate we interviewed was the one we hired — and she rebuilt our RAG pipeline in a month.”
Daniel WeberCTO, Parcelbase“We didn't need a whole team, we needed one senior ML engineer who could own the problem end to end. That's exactly who we got.”
Sofia MarchettiHead of Product, Klarvo“Embedded talent that actually feels in-house. Daily standups, our tooling, our codebase — zero agency friction.”
James WhitfieldFounder & CEO, Cadenza Health“Their engineer cut our inference costs by 40% in the first six weeks. The engagement paid for itself before it ended.”
Clara JensenDirector of Data, Loopwise“From brief to signed contract in under two weeks. The shortlist had three candidates, and honestly all three were hireable.”
Marcus ReinholtChief Product Officer, Ferrostack“Our computer-vision backlog was stuck for months. Eventum's specialist unblocked it in the first sprint and mentored the team along the way.”
Viktor HaldenEngineering Manager, Brightlane
Why hire through Eventum
Why hire through Eventum
01
Production applications, not demos
We filter for engineers who have shipped AI-powered products, internal tools, agent workflows, and LLM integrations in real environments — with users, edge cases, monitoring, and handoff.
02
Full-stack AI execution
Our screening looks for practical ability across frontend, backend, APIs, model integration, tool use, retrieval, auth, deployment, and production tradeoffs — not just prompt experiments.
03
Fast shortlist, senior signal
You get a focused shortlist of engineers who can turn AI capability into working software — not generic app developers relabeled as “AI talent.”
Dmitrii S.
AI Application Engineer · 10 yrsFull-stack engineer who builds LLM-powered products, agent workflows, and internal tools that integrate with real business systems. Strong across product UX, backend orchestration, APIs, auth, and production deployment.
TypeScriptNext.jsPythonOpenAIAgents
Results
Shipped a customer-facing AI assistant from prototype to production in 8 weeks.
Previously Worked at:
Lena K.
Full-Stack GenAI Engineer · 7 yrsBuilt AI workflow tools for operations, support, and content teams. Experienced in connecting LLMs to internal systems, designing review loops, and turning ambiguous AI concepts into usable software.
ReactFastAPILangChainPostgresVercel
Results
Built an internal AI review tool that reduced manual triage time by 52%.
Previously Worked at:
GenAI integration for a creative AI platform
A fast-growing creative AI platform needed senior engineering support to integrate advanced generative AI capabilities into an existing production product. Eventum helped identify AI application talent with the right mix of backend, product, and GenAI integration experience.
- Production AI product integration
- Backend and application-layer ownership
- GenAI model workflow implementation
- Clear communication with product and engineering teams
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