001
GenAI product development
Build customer-facing or internal AI products with real workflows, usable interfaces, and production-ready architecture.
Role overview
What we expect 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 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 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:
A production AI assistant where the team already works
A financial-analysis team was losing hours moving between data sources and the conversations where decisions actually happened. Eventum built a Slack-native LLM assistant connected to their internal data, so analysts could pull grounded answers without leaving the channel. This was application engineering, not a model demo: authentication, data access, response quality, and error handling all had to hold up in daily use. The assistant now handles four distinct workflows the team runs every day.
- 90% faster insight retrieval
- 4 workflow capabilities integrated
- Live in the team's existing Slack
Related roles
Other roles often hired alongside AI Application Engineers
Related case studies
