Senior Machine Learning Engineer

Unifonic

WorldwideremotePosted 8 days ago

Skill Required

Machine-Learning-EngineeringAI-EngineeringNLP-EngineeringData-ScienceSoftware-EngineerSenior-ML-EngineeringRAG-EngineeringGenerative-AISenior-ML-EngineerSenior-AI-ML-EngineerSenior-Machine-Learning-Data-ScientistSenior-Machine-Learning-ScientistSenior-Data-Science-EngineerMachine Learning EngineerMachine Learningsoftware engineeringGenerative AIStatisticsdata engineeringData StructuresScikit-learnLangChainEngineeringTensorFlowKubernetesanalyticaldevelopingdesigningRabbitMQbuildingPyTorchTestNGPythonPandasDockerdesignAgileFlaskNumPyFulltime

Key highlights

Unifonic share scheme (we are all owners!)
30 holiday days after the first anniversary
Your Birthday off!
Spend up to 25 days per year working from anywhere in the world
Paid leave for new parents
Competitive salary and bonus

Role overview

We are a dynamic SaaS startup voted a Great Place to Work®, revolutionizing business communication with a passionate team of 500 Unifones serving 5000+ customer-centric companies. Our Engineering team designs and maintains the systems driving Unifonic's solutions, and we are seeking a Senior Machine Learning (AI) Engineer to lead the end-to-end development and deployment of advanced machine learning solutions across NLP, RAG, LLMs, recommender engines, and anomaly detection.

Responsibilities

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.
Researching, evaluating, and integrating state-of-the-art NLP models and RAG frameworks (e.g., Transformers, BERT, GPT variants, Vector Databases, Semantic Search).
Mentoring junior team members on the team, sharing knowledge, and advising the best machine learning and software engineering practices and approaches.
Establishing and maintaining robust communication channels with other cross-functional teams to facilitate the integration of machine learning solutions into other Unifonic products.
Developing and optimizing highly confident machine learning algorithms and models and creating/exposing the service APIs using frameworks such as Flask, FastAPIs, or other relevant frameworks.
Staying up to date with the latest machine learning research papers, and AI trends (i.e. Generative AI).
Collaborating with the data engineering team and other teams to collect and analyze extensive datasets, extracting insights and patterns, in real-time, near-real-time, or batch processing mode.
Implementing proof of concepts and prototypes to demonstrate the potential of new AI use cases and innovations.
Building scalable, maintainable machine learning services, which should handle thousands of requests per second, and help to perform the required load tests to meet the SLA.
Reviewing the code of other team members and suggesting improvements to ensure the SOLID principles and clean architecture.
Assisting in the project documentation and demos.

Requirements

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.
Familiar with code versioning tools such as GIT, CI/CD concepts, and toolchains.
Familiar with Agile methodologies i.e. scrum and kanban.
Ability to develop high-level architecture and low-level design, End-to-end for a specific project.
General knowledge of Data warehouse tools e.g. Vertica is a plus.
A Bachelor's degree in a relevant field. (e.g. Computer Science, Computer Engineering, Software, etc).
Excellent communication and collaboration skills.
Good level of spoken and written Arabic and English.

Nice to have

Basic knowledge of Kubernetes and Docker is nice to have.
Experience fine-tuning pre-trained models and using vector search to enhance LLMs results.
Experience with the Hugging Face libraries (i.e. transformers).
Experience with LLM frameworks (i.e. LangChain) and prompt engineering techniques.
Experience in event sourcing patterns and tools i.e. Kafka, RabbitMQ, or similar is nice to have.
Experience with LLM frameworks (i.e. LangChain) and prompt engineering techniques is nice to have.
Experience in event sourcing patterns and tools i.e. Kafka, RabbitMQ, or similar is nice to have.