AI Engineer $$$

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

Hiring process: recruiter outreach > interview > test task > final interview > job offer.