Senior AI Engineer (Product Neuro Forge Team)
AppFollow is an app review management and ASO platform. Our main goal is to ease the everyday routines of app developers, product managers, marketing teams, customer support, etc. AppFollow helps you gather and manage your apps and games data, increase app average rating, improve app store rankings, and app user loyalty.
Ratings and reviews are our core data, and AI is how we turn them into value for our customers, helping them automate routine work with user feedback and save time: feedback categorization, review summarization, AI-generated replies, semantic search, anomaly detection, and conversational insights.
This fully remote role is for a Senior AI Engineer who will drive these capabilities end-to-end, from research and prototyping to production. You'll work with both commercial LLMs and open-source models, build training and evaluation pipelines, and ship ML-powered features used worldwide by app and game teams, as well as anyone working with digital user feedback.
About the Role- Own AI/ML features end-to-end: research, prototype, production, monitoring and iteration
- Design and build LLM-powered features on top of reviews and ratings data: feedback categorization, summarization, reply generation, semantic search, anomaly detection, conversational and agentic scenarios
- Work with commercial LLM APIs (OpenAI, Anthropic, Google) as well as open-source models (Llama, Mistral, Qwen, etc.): model selection, adaptation, fine-tuning, and deployment
- Build and maintain pipelines for model training, fine-tuning, and quality evaluation: datasets, metrics, offline evals, LLM-as-a-judge, A/B tests
- Develop RAG and semantic search capabilities: embeddings, vector storage, retrieval quality
- Optimize quality, latency, and cost of LLM inference in production
- Track state-of-the-art in NLP/LLM, run experiments and POCs, and turn the promising ones into product features
- Collaborate with backend, product, and platform teams; contribute to the overall system architecture; write efficient, testable, secure, and documented code
- 5+ years of software development experience; strong production Python (asyncio)
- 3+ years of hands-on ML/NLP experience with models shipped to production
- Practical experience with LLMs: prompt engineering, RAG, fine-tuning open-source models (LoRA/PEFT), working with both commercial APIs and self-hosted models
- Experience building model quality evaluation processes: metrics, eval datasets and pipelines, A/B testing
- Confidence with the PyTorch and Hugging Face ecosystem (transformers, datasets, PEFT)
- Proficiency in FastAPI for API development
- Strong SQL skills (MySQL or PostgreSQL), experience with ORM frameworks (preferably SQLAlchemy)
- Experience with unit testing (pytest)
- Upper-intermediate English or higher
- Experience serving open-source LLMs in production (vLLM, TGI, Triton) and working with GPU infrastructure
- Experience with vector databases (e.g. pgvector)
- Experience with agentic and orchestration frameworks (LangChain, LangGraph) and eval/observability tooling (MLflow, Langfuse)
- Experience with data processing pipelines and automation (e.g. Airflow, Prefect)
- Experience with cloud-based services (AWS), NoSQL databases (MongoDB), message brokers (RabbitMQ, Kafka)
- Classical ML/NLP background (text classification, clustering, topic modeling)
- Open-source contributions, publications, or pet ML projects you're proud of
- Full-time remote job. Though you're always welcome to spend time with us in monthly All hands in our hubs: Helsinki, Belgrade, Tbilisi, Batumi, Yerevan
- Paid Vacation and Sick leaves. Take the time you need to stay motivated, charged, and balanced. By prior agreement, you can have days off for special occasions
- Generous social benefits package including health insurance, equipment reimbursement, home office moderation bonus, and many more
- Stock options bonus according to the employee stock ownership plan
- You'll have executive-level visibility into how the company is run and performing. We are always ready to provide dedicated support and fast-track your onboarding, including giving you the tools you need to be successful.
Hiring process
- HR screening interview — 15 min
- Backend Technical interview — 90 min
- ML Technical interview — 90 min
- Culture fit interview — 60 min
- Recommendations check
Expected timeline: 2–4 weeks from application to offer.
Hint
Want to increase your chances? Please ensure your LinkedIn profile is complete, up to date, and accurately reflects your experience before submitting your application.
