AI/ML Engineer

We are looking for an AI/ML Engineer to own and evolve our AI core: tuning local LLM models, designing and optimizing RAG pipelines, improving answer quality, and boosting performance across different hardware profiles (from powerful servers to more constrained environments).

Experience in cybersecurity will be a strong advantage.

Key Responsibilities

• LLM tuning and adaptation
  • Fine-tune / LoRA / PEFT local models for our domain (cybersecurity, log data, detection rules, flows).
  • Design and improve prompt engineering, system instructions, and configurations for stable, predictable answers.
  • Analyze and reduce hallucinations, implement guardrails and safety mechanisms.
• RAG and knowledge workflows
  • Design and implement RAG pipelines: chunking strategies, embeddings, indexing, reranking, and multi-step retrieval.
  • Integrate vector databases.
  • Build and track quality metrics for RAG (precision/recall on Q&A sets, domain-specific qualitative evaluation).
• Performance & latency optimization
  • Profile and optimize model inference for different hardware setups (CPU/GPU, various memory/compute limits).
  • Apply quantization, pruning, distillation (e.g., 4-bit/8-bit quantization, GPTQ, AWQ) to speed up inference.
  • Implement caching strategies, batching, and efficient query patterns for vector DBs and LLM gateways.
• Product integration
  • Work closely with the backend team to deploy models to production (REST/gRPC services, microservices).
  • Set up MLOps pipelines: automated deployment, monitoring, A/B testing of models and configurations.
  • Participate in the design of new AI features for SOC analysts, detection engineers, and threat researchers.
• Evaluation & experimentation
  • Run experiments to compare models, architectures, and configurations.
  • Build and maintain datasets (including synthetic data) for training, fine-tuning, and benchmarking.
  • Maintain clear technical documentation for models, pipelines, and evaluation frameworks.

Requirements

• 3+ years of experience as an ML Engineer / AI Engineer / Data Scientist working with production ML systems.
• Strong Python skills; experience with PyTorch / TensorFlow / JAX (preferably PyTorch).
• Practical experience with LLMs / NLP:
• working with open-source models (OSS, Llama, Mistral, Gemma, Qwen, etc.);
• adapting, fine-tuning, or instruction-tuning models.
• Experience building RAG solutions:
• embeddings (OpenAI, SentenceTransformers, or local embedding models);
• vector databases search.
• Solid understanding of core ML concepts: optimization, regularization, evaluation, and handling noisy real-world data.
• Experience with Docker, databases, Git, and CI/CD.
• Ability to read and write technical documentation in English (Intermediate+ level).

Strong Pluses

• Background in cybersecurity, for example:
• hands-on work with SIEM / SOAR / XDR platforms;
• understanding of log sources (Windows Event Logs, Sysmon, EDR), MITRE ATT&CK, detection rules (Sigma, YARA, etc.).
• Experience building AI solutions that:
• generate or transform code, queries, or detection rules;
• process large volumes of technical documentation and reports.
• Hands-on model optimization:
• distillation, quantization, mixed-precision inference, and GPU profiling.
• Contributions to open-source projects or notable personal projects in LLM/AI.
  
  Soft Skills:
   
• Ability to work independently, strong analytical thinking
• Team player with a sense of responsibility

• Willingness to participate in code reviews, refactoring, and writing technical documentation

   

   

  What We Offer:
   

• Competitive salary
• Paid annual leave
• Medical insurance
• Minimum bureaucracy
• Remote work
• Work in a product company
• Team of strong IT professionals working in a vibrant product environment.
  
  Working conditions:
   
• 5-day work week
• 8-hour working day (flexible schedule).