Senior AI Backend Engineer (Product Neuro Forge Team)

AppFollow is an App review management and ASO platform.

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 Backend Engineer who will drive these capabilities end-to-end, from prototyping to production. You'll work with both commercial LLMs and open-source models, build evaluation pipelines, and ship ML-powered features used worldwide by app and game teams, as well as anyone working with digital user feedback. This is an applied engineering role at heart: your models won't stay in notebooks. Together with our backend team, you'll also develop the product functionality, services, and APIs around them. It's not a research or data science role: you won't train models from scratch, and fine-tuning experience is a plus, not a must.

About the Role

• Own AI/ML features end-to-end: 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

• Develop and optimize AppFollow product functionality beyond ML: services and internal and public APIs to core data entities (apps, reviews, ratings)

• Contribute with other developers to the overall system architecture; design, build, and document an efficient, testable, reliable, secure, and scalable codebase

• Develop RAG and semantic search capabilities: embeddings, vector storage, retrieval quality

• Optimize quality, latency, and cost of LLM inference in production

• Build and maintain pipelines for quality evaluation: datasets, metrics, offline evals, LLM-as-a-judge, A/B tests; where it pays off, also for model training and fine-tuning

• 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

• Integrate new features with the existing system, including legacy services, and collaborate with the Platform team to deploy to various environments and ensure smooth operation

• Track state-of-the-art in NLP/LLM, run experiments and POCs, and turn the promising ones into product features

About you

• 5+ years of software development experience; strong production Python (asyncio)

• Proficiency in FastAPI for API development

• Strong SQL skills (MySQL or PostgreSQL), experience with ORM frameworks (preferably SQLAlchemy)

• Experience with unit testing (pytest)

• 3+ years of hands-on ML/NLP experience with models shipped to production; integrating, serving, and evaluating models counts, not only training them

• Practical experience with LLMs: prompt engineering, RAG, working with both commercial APIs and self-hosted models

• Experience building model quality evaluation processes: metrics, eval datasets and pipelines, A/B testing

• Working knowledge of PyTorch and the Hugging Face ecosystem (transformers, datasets)

• Upper-intermediate English or higher

It would be nice to have

• Experience fine-tuning open-source models (LoRA/PEFT)

• 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

Benefits we offer

• 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.

The biggest benefit is our awesome AppFollow team. We're a team of open-minded and friendly high-skilled professionals that enjoy creating a great product, growing together, and supporting each other.

Jump on the board!

Hiring process

• HR screening interview — 15 min

• Backend Technical interview — 90 min

• AI Engineering interview — 60 min

• Culture fit interview — 60 min

• Recommendations check

Expected timeline: 2–4 weeks from application to offer.

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How To Recognise And Avoid Employment Scams

We’ve noticed an increase in fake job postings and fake job offers aimed at gathering personal information. Be aware that all official AppFollow recruitment emails come exclusively from an @appfollow.io domain. Our interviews are conducted either over video calls or in person; we never conduct interviews via text or chat. If you’re unsure about the legitimacy of a job offer or opportunity from AppFollow, please reach out directly to us at people@appfollow.io for verification.