Machine Learning Engineer (LLMs)

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Requirements:

• 3+ years experience with deployment and maintenance of Machine Learning models in production
• Experience in deploying and maintaining Deep Learning models, particularly Large Language Models (LLMs)
• Strong command of writing production-level code in Python, with a focus on best engineering practices, in particular for training & deploying models.
• PyData stack along with quick frontend frameworks e.g. streamlit.
• Proven expertise in Cloud Computing (preferably AWS and services like IAM, EC2, S3, ECR, EKS, Redshift, Athena, Glue, Lambda, SecretManager) for storage, data pipelines, ML pipelines, and ML deployment.
• Machine Learning frameworks such as: Tensorflow, PyTorch, JAX, scikit-learn, Transformers (HuggingFace).
• Proven track record of development of data and machine learning pipelines.
• Knowledge of Linux/Unix system, shell scripting.
• Parallel computing (multi-processing, async, GPUs, types of AI parallelism).
• Culture of DevOps and high-quality software standards.
• Fluency in English.

Responsibilities:

• Operationalization of Machine Learning Models
• Orchestration of the entire ML model lifecycle, from development and deployment to monitoring, maintenance, and optimization ensuring scalability, efficiency, and reliability.
• Implementation of automated workflows for model retraining, versioning, and performance tracking to ensure long-term stability.
• Transformation of Machine Learning artifacts into production systems and services maintaining robust integration with existing engineering infrastructure.
• Tooling, Infrastructure & Experimentation Support
• Design and implementation of tools, frameworks, and infrastructure to enhance efficiency of Data Scientists and other stakeholders simplifying areas such as model training and evaluation, data annotation, and processing.
• Working with large-scale datasets in structured and ad-hoc exploratory setups to support both creation of well-organized data pipelines and rapid experimentation and prototyping.
• Supporting Technical Lead and Data Scientists in refactoring and optimizing research code, ensuring high-quality, reusability, and scalability of delivered solutions bridging the gap between AI experimentation and real-world deployment.
• Continuous Learning
• Staying up to date with cutting-edge advancements in AI technology, including state-of-the-art models, algorithms, tools, and frameworks (both models/algorithms and tools/libraries/SaaS/APIs, etc.).
• Exploring opportunities to incorporate new methodologies, libraries, and services that enhance Brainly’s AI capabilities.