We are seeking two highly qualified AI/ML Engineers to join our R&D department and drive the development of Artificial Intelligence solutions for Layer8 products. You will drive innovation by researching and integrating cutting-edge AI capabilities, transforming them into user-facing product features and optimized internal workflows. Working within a modern data architecture, you will leverage our Data Lakehouse and Kubernetes environments to prototype, develop, fine-tune, deploy, and scale these AI solutions.
These positions are funded and aligned with the scope and objectives of the Layer8's PRR IFIC project.
Key Responsibilities
Applied Research & Feature Development: Conduct research into emerging AI and Machine Learning methodologies to design, prototype, and develop innovative features for our product suite.
LLM Fine-Tuning & Customization: Fine-tune Large Language Models (LLMs) on domain-specific data to enhance performance, accuracy, and relevance for targeted project use cases.
Algorithm Development: Translate complex business and cybersecurity problems into viable AI-driven solutions, building robust algorithms from the ground up.
Model Deployment: Deploy, apply, and maintain machine learning models in a production environment using scalable, containerized deployments.
Big Data Processing: Interact with our Data Lakehouse using Apache Spark to process large volumes of data and ensure clean, efficient data pipelines for model training and inference.
System Integration: Integrate AI tools, RAG pipelines, and customized models into internal systems using Python/Java to drive productivity and efficiency.
Cross-functional Collaboration: Work closely with multidisciplinary teams to ensure that R&D outcomes are successfully integrated into the final product roadmap.
Required Qualifications (Must-Haves)
Education: Master’s degree in Computer Engineering (Engenharia Informática), Computer Science, or a related field.
Programming Languages: Proficiency in Python and/or Java.
Core ML & AI: Proven experience building models with TensorFlow, PyTorch, and Scikit-learn, backed by a solid understanding of algorithms, data structures, and statistics.
Deployment & Infrastructure: Hands-on experience developing and deploying applications using Docker.
Research Acumen: Ability to read, understand, and implement methodologies from academic or technical research papers into functional code.
Preferred Qualifications & Tech Stack (Nice-to-Haves)
Big Data Ecosystem: Strong familiarity big data processing using Apache Spark.
Generative AI & Modern Architectures: Experience working with Large Language Models (LLMs), including fine-tuning techniques (e.g., PEFT, LoRA), Natural Language Processing (NLP), Vector Databases, and building Retrieval-Augmented Generation (RAG) pipelines.