AI Engineer
appflame ·
- Category
- AI engineering
- Experience
- 1+ years
- Company size
- 501-1000
appflame ·
Build and scale an AI-powered productivity analytics platform using LLMs (Claude, GPT) to generate structured reports, automation scripts, and insights with self-correcting workflows and robust evaluation frameworks.
appflame — Ukrainian product-driven tech company building world-class products: Hily, Taimi, AdConnect, Mailkeeper, and more.
About us:
7 years in the market, 500+ team members, offices in Kyiv, London, Limassol, and a co-working hub in Warsaw.
In 2025, appflame ranked 5th among the top 50 employers in Ukraine according to Forbes and won the “Best Place for Growth” award.
Our apps Hily and Taimi are among the top 5 dating apps in the US with over 70 million users. AdConnect and Mailkeeper focus on building proprietary B2B and B2C solutions.
Our mission:
To put Ukrainian-built products on the global map.
Our goals:
Break into the global top 5 product companies.
Become a unicorn.
Make Ukraine a country where unicorns are born.
We’re looking for bold, driven people who are passionate about building real products and dream of launching and scaling great startups. You bring the ambition — we provide the environment to make it happen.
What you’ll do:
Design and develop an AI-powered productivity analytics platform — from data pipeline architecture to the final analytical product that helps teams make data-driven decisions.
Build scalable LLM pipelines (Claude, GPT): develop data chunking strategies, implement MapReduce approaches for parallel processing of large datasets, and synthesize results into structured reports and insights.
Create a meta-workflow system where LLMs generate, test, and deploy automation scripts in an isolated environment — with automatic self-correction loops and production deployment without manual intervention.
Develop system-level prompt engineering: build and maintain a library of prompt templates for various analytical scenarios — from summaries and profiles to deep performance analysis.
Build an evaluation framework for AI output quality control: hallucination detection, consistency scoring, regression tests — ensuring the product delivers reliable and reproducible results.
Scale the platform to new domains and analysis types without linear growth in manual effort — through an architecture that allows adding new modules via configuration, not code.
Document AI architecture, define automation specs, and present product insights to stakeholders and clients.
It’s a match if you have:
2+ years of experience working with LLMs in production: prompt engineering, pipeline development, API integration — with at least 1 year of hands-on experience with advanced features (tool use, structured outputs, agents).
Strong Python skills (async, dataclasses, type hints, API integrations) and a commitment to writing clean, testable, and maintainable code.
Understanding of MapReduce patterns for LLM processing: ability to choose chunking strategies, organize parallel processing, and aggregate results into a cohesive analytical product.
Experience building agentic systems: tool use, self-correcting loops, multi-agent workflows — and the judgment to know when an agent works better than a rigid pipeline.
Proficiency in SQL and experience working with analytical databases.
English at C1 level — comfortable reading documentation, writing technical specs, and communicating asynchronously.
Ownership mentality: you take tasks end-to-end, debug production issues independently, and iterate to deliver results without micromanagement.
Nice to have:
Experience with orchestration/automation platforms (Windmill, Dagster, Prefect) — understanding how to build reliable automated workflows.
Knowledge of RAG architectures, vector databases, and embedding pipelines — ability to build systems that work with large volumes of unstructured data.
Experience building evaluation systems for LLMs (LangSmith, PromptFoo, or custom solutions) — understanding how to measure and improve AI product quality.
Familiarity with Databricks / Delta Lake or Snowflake — experience working with modern data platforms.
Experience working at product-driven tech companies or AI startups where you needed to build and iterate quickly.
Understanding of product team metrics (DAU, retention, unit economics) and the ability to connect technical decisions to product impact.
Preferred tech stack:
Core:
Languages: Python (primary), SQL
LLM APIs: Claude API (Anthropic), OpenAI API
Databases: PostgreSQL, ClickHouse
Infrastructure: Docker, Git, FastAPI, Pydantic, pytest, asyncio
Nice to have:
Languages: TypeScript/JavaScript, Bash
LLM APIs: Google Gemini API
Orchestration & Automation: Windmill, Dagster, Prefect, Airflow, Temporal
RAG & Embeddings: LangChain, LlamaIndex, ChromaDB, Pinecone, Weaviate, pgvector,FAISS
Eval & Observability: LangSmith, PromptFoo, Weights & Biases, Arize AI
Databases: Redis, MongoDB
Data Platforms: Databricks, Delta Lake, Snowflake, BigQuery
Infrastructure: Kubernetes, AWS (Lambda, SQS, S3), GCP
CI/CD & DevOps: GitLab CI, GitHub Actions, Terraform