Overview
We are seeking a highly skilled Machine Learning Engineer with expertise in Deep Learning, Natural Language Processing (NLP), and Large Language Models (LLMs). You will be responsible for designing, building, and deploying advanced ML models and pipelines, ensuring scalability, performance, and production readiness. The adecuado candidate has strong research knowledge combined with hands-on engineering skills to deliver intelligent, enterprise-grade AI solutions.
Details
Location: Remote in EU
Employment Type: Full-Time, B2B Contract
Start Date: ASAP
Language Requirements: Fluent English
Key Responsibilities
Design, develop, and optimize ML models with a focus on deep learning, NLP, and LLM-based applications.
Build scalable pipelines for training, fine-tuning, evaluation, and deployment of models.
Work with frameworks such as PyTorch, TensorFlow, and Hugging Face Transformers.
Fine-tune and adapt pre-trained LLMs (GPT, BERT, LLaMA, etc.) for domain-specific tasks.
Develop solutions for text classification, summarization, embeddings, RAG, and conversational AI.
Ensure model scalability, robustness, and low-latency performance in production environments.
Collaborate with data engineers to prepare and optimize large-scale datasets.
Implement MLOps practices (CI/CD, monitoring, retraining, governance).
Participate in code reviews, documentation, and technical knowledge sharing.
Requirements
5+ years of experience in machine learning, with at least 3+ years focused on deep learning/NLP.
Strong expertise in PyTorch or TensorFlow, and NLP frameworks (Hugging Face, spaCy, NLTK).
Hands-on experience with LLMs (GPT, T5, LLaMA, Falcon, etc.), fine-tuning and prompt engineering.
Proficiency in Python and libraries (NumPy, Pandas, Scikit-learn).
Experience with MLOps tools (MLflow, Kubeflow, SageMaker, Vertex AI, or Azure ML).
Strong understanding of transformer architectures, embeddings, and attention mechanisms.
Familiarity with cloud platforms (AWS, Azure, GCP) for ML deployment.
Excellent problem-solving and debugging skills.
Nice to Have
Experience with vector databases (Pinecone, Weaviate, Milvus) for semantic search.
Knowledge of retrieval-augmented generation (RAG) pipelines.
Exposure to multimodal ML (text + image/audio/video).
Contributions to open-source ML/NLP projects.
Advanced degree (MSc/PhD) in Computer Science, AI, or related field.
Industry background in fintech, healthcare, telecom, or e-commerce.
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