Python Backend Developer (Lm/Llm)

This position is an excellent opportunity for candidates who want to work in a dynamic start-up environment and gain hands-on experience building practical, production-ready AI systems.

Key Highlights:

·         Location: Remote (Türkiye)

          Type: Full-time | Work from home

Job Description

We’re looking for a developer who is strong in Python, understands how ML/NLP and LLM works, and is excited to help build datasets, training pipelines, and backend services that bring models into production.

• Develop and maintain Python backend services that support AI features (APIs, pipelines, background jobs).
• Work on data and dataset workflows, including data collection, cleaning, labelling, versioning, and evaluation.
• Implement and improve ML/NLP pipelines, including pre-processing, feature/embedding workflows, model evaluation, and iteration loops.
• Support model improvement and adaptation approaches when needed:
  • LoRA / QLoRA (parameter-efficient fine-tuning)
  • Full fine-tuning (when appropriate)
• Contribute to retrieval-based AI patterns when relevant (RAG, embeddings, vector search, etc.).
• Write clean, testable code and help improve engineering quality in a start-up environment.

Technologies used: Python, FastAPI/Flask, Pandas, PyTorch/Transformers, vector search (FAISS/pgvector, etc.), Postgres, Redis, Docker, GitHub.

Required Qualifications

• 0–3 years of software development experience (including internships and projects).
• Strong Python fundamentals (clean code, debugging, modular design; basic typing is a plus).
• Practical understanding of how ML works (training vs inference, overfitting, model evaluation, data quality).
• Basic understanding of NLP fundamentals (tokenization, embeddings, evaluation concepts).
• Motivation to learn quickly and develop end-to-end systems (not limited to notebooks).
• Basic understanding of LLM fundamentals.

Preferred Qualifications

• Familiarity with LLM applications (prompting, evaluation, limitations).
• Familiarity with RAG concepts (retrieval, chunking, embeddings, vector search).
• Experience with fine-tuning workflows (LoRA/QLoRA or full fine-tuning).
• Experience with vector search/databases (FAISS, pgvector, Pinecone, Weaviate, Elasticsearch).
• Backend development experience (FastAPI/Flask, REST APIs, async, background jobs).
• Familiarity with MLOps tools (Docker, CI/CD basics, monitoring, experiment tracking).
• Interest or experience in Python-based cybersecurity projects (bonus).
• Hands-on cybersecurity experience (Python security tooling, security automation, CTFs, secure development practices) (bonus).

Success Criteria

• Ability to take a feature from idea › dataset/pipeline › backend endpoint › measurable results.
• Deliver reliably, communicate clearly, and improve code quality over time.
• Build a strong foundation in practical applied ML engineering, not just theory.

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