AI ENGINEER - REMOTO

AI ENGINEER - REMOTO
Must-Have
• Strong Python development skills, including production-grade API and service development
• Solid knowledge of Machine Learning fundamentals, model development workflows, feature engineering, validation, and evaluation
• Hands-on experience with PyTorch and scikit-learn
• Strong understanding of Large Language Models (LLMs), Transformer architectures, tokenization, embeddings, and inference workflows
• Experience with LangChain and LangGraph for building LLM-powered and agentic applications
• Design and implementation of multi-agent and agentic AI systems, including orchestration, tool usage, state management, and guardrails
• Experience designing Retrieval-Augmented Generation (RAG) solutions, including chunking, embedding strategies, retrieval, reranking, and context management
• Experience with vector databases, preferably Qdrant, and caching/state-management technologies such as Redis
• Knowledge of fine-tuning techniques for LLMs and deep learning models, including parameter-efficient approaches such as LoRA/PEFT
• Knowledge of AI hardware stacks for development and inference, including GPUs, VRAM constraints, CUDA fundamentals, and performance-aware model deployment
• Ability to design scalable, efficient, reliable, and high-performance AI solutions, with attention to latency, throughput, cost, memory usage, and observability
• Microservices architectures
• Algorithms, data structures, and selection of the solution best suited to the context
• Docker (Dockerfiles, Docker Compose) and containerized AI workloads
• CI/CD practices for software and ML/AI systems
• Distributed systems design and integration of AI services into production architectures
• Experience in Agile environments
• Fluent English
Nice-to-Have
• Hugging Face ecosystem (Transformers, Datasets, Accelerate, PEFT)
• LLM serving and inference frameworks such as vLLM and Ray
• MLOps / LLMOps practices, including experiment tracking, model versioning, evaluation pipelines, and model lifecycle management (e.g., MLflow)
• LLM observability, tracing, and evaluation platforms such as Phoenix
• Prompt engineering and systematic evaluation of prompts, agents, and RAG pipelines
• Knowledge of model quantization, batching, caching, distillation, and other inference-optimization techniques
• Familiarity with NVIDIA GPU tooling and profiling, and with selecting appropriate hardware for training, fine-tuning, and inference workloads
• Familiarity with Kubernetes and GPU-enabled container orchestration
• Knowledge of data pipelines and orchestration tools such as Dagster
• Understanding of data quality, dataset curation, synthetic data, and responsible handling of training/evaluation data
• Familiarity with AI security topics including prompt injection, data leakage, model abuse, access control, and the NIST Cybersecurity Framework
• Experience with automated testing for AI systems, including unit, integration, regression, evaluation, and performance testing
• GitLab CI and artifact/package registries
Personal Skills
• Ability to work effectively in dynamic and evolving technical environments
• Strong problem-solving and analytical skills
• Team spirit and ability to collaborate across software, data, infrastructure, and product teams
• Commitment to continuous learning and knowledge sharing in a rapidly evolving AI ecosystem
• Proactive approach, ownership, and sense of responsibility
• Ability to balance experimentation with production quality, maintainability, security, and business constraints

Work arrangement
Yes