Senior Machine Learning Engineer

Proudly voted a Great Place to Work®, we are a dynamic startup in the SaaS space that is revolutionizing the way businesses communicate. Our team is made up of 500 energetic and passionate Unifones who are dedicated to delivering the best possible experience to 5000+ customer-centric companies.

We pride ourselves on our fun and collaborative work environment, where creativity and new ideas are constantly encouraged. As shareholders in the business, we're so much more than a group of passionate communicators. We are Unifones. Join our team and be a part of something big!

Meet the team!

Our Engineering team is responsible for designing, developing, and maintaining the systems and technologies that drive Unifonic's solutions. We work closely with other departments to ensure our products and services meet the needs of our customers. If you are passionate about technology and are excited about working on cutting-edge communication and engagement solutions, we want you on our team.

As a Senior Machine Learning (AI) Engineer, you will be responsible for designing, developing, and deploying advanced machine learning solutions across various domains, including NLP, Text Classification, RAG, LLMs, Recommender engines, and Anomaly detection. This role involves end-to-end project ownership, from data preprocessing to the creation of service APIs, and offers opportunities to work on cutting-edge AI technologies.

Help us shape the future of communication by

• Leading the end-to-end design, development, and deployment of robust and scalable machine learning solutions, with a strong emphasis on NLP and RAG architectures.
• Architecting and implementing RAG systems, combining large language models (LLMs) with robust retrieval mechanisms to improve the accuracy, factual grounding, and interpretability of generated content.
• Applying advanced NLP techniques for tasks such as text classification, entity recognition, sentiment analysis, summarization, question answering, and information extraction.
• R…

What you'll bring

• Proven experience designing and implementing RAG systems, including familiarity with various retrieval strategies (e.g., BM25, dense retrieval, hybrid approaches) and knowledge graph integration.
• Hands-on experience with LLM orchestration frameworks such as LangChain, LangGraph, CrewAI, or similar tools for building and managing autonomous agents.
• Deep expertise in various NLP techniques and models, including but not limited to:

Transformer architectures (e.g., BERT, GPT, T5, LLama, Mistral)

Large Language Models (LLMs) and their fine-tuning/adaptation

Vector embeddings and similarity search

Text classification, named entity recognition (NER), sentiment analysis, summarization, and question answering.

• Hands-on 3-5 years of relevant work experience as a Machine Learning Engineer.
• Hands-on 3+ years of experience with Python.
• Excellent analytical abilities, with the capacity to collect, organize, and analyze large datasets to glean valuable insights.
• End-to-end experience in training, evaluating, testing, and deploying machine learning products in production.
• Ability to write world-class code in Python (SOLID principles), considering the best software engineering fundamentals, i.e. data structures, algorithms, and data modeling
• Solid experience in ML frameworks such as NumPy, Pandas, Scikit-Learn, PyTorch, Keras, BERT, Tensorflow, and similar.
• Familiarity with MLOps best practices, e.g. Model deployment and reproducible research.
• Mastering data science needed skills like SQL, hypothesis testing, Data cleansing, data augmentation, data pre-processing techniques, and dimensionality reduction.
• Basic knowledge of Kubernetes and Docker is nice to have.
• Excellent understanding of Machine learning techniques like Naive Bayes classifiers, SVM, Decision Tree, KNN, K-means, Random Forest, modeling and optimization, evaluation metrics, classification, and clustering.
• Experience with the Hugging Face libraries (i.e. transformers).
• Experience fine-tuning pre-trained models and using…