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AI Engineer

Turn large language models into real business systems. Build intelligent architectures that scale beyond experimentation.

Santa Maria da Feira, Portugal [On location]
Full Time

Objective

Lead the design and implementation of advanced Artificial Intelligence solutions, focusing on strategic integration of Large Language Models (LLMs) and creating intelligent systems that solve complex business challenges for Umain's clients.

Responsibilities

  • Develop and optimize RAG (Retrieval-Augmented Generation) architectures using vector databases to ensure model accuracy and context
  • Implement autonomous agents and AI workflows using orchestration frameworks like LangChain, LlamaIndex, or Haystack
  • Perform integration, fine-tuning, and evaluation of models on the IBM Watsonx.ai platform, as well as other ecosystems (OpenAI, Anthropic, or Open Source models via Hugging Face)
  • Develop Prompt Engineering strategies and quality and performance monitoring systems for models in production (hallucination and drift detection)
  • Collaborate with Architecture/Solutions teams to ensure AI models are scalable, secure, and computationally efficient

Technical Requirements and Profile

  • Consolidated Experience: Minimum 5 years in Software Engineering, with proven focus on Machine Learning and Natural Language Processing (NLP) projects
  • Programming Mastery: Advanced proficiency in Python and mastery of structural libraries (Pandas, NumPy, Scikit-learn, PyTorch, or TensorFlow)
  • GenAI Ecosystem: Practical experience implementing LLMs (GPT-4, Claude, Llama 3) and model context and memory optimization techniques
  • Data Architecture: Deep knowledge in vector databases (ChromaDB, Pinecone, Weaviate, or Milvus) and indexing and semantic search techniques
  • Evaluation and Governance: Experience with AI quality evaluation frameworks (e.g., Ragas, Giskard) and AI Governance concepts (explainability and ethics)
  • Integration and DevOps: Familiarity with model deployment in containers (Docker/Kubernetes) and MLOps practices for AI lifecycle automation
  • Cloud: Knowledge of Cloud AI services (IBM Cloud, AWS, or Azure)
  • Soft Skills: Mandatory English fluency; high analytical capacity and enjoyment of technical sharing environments, mentoring, and continuous learning

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