Principal Machine Learning Engineer (MLE)

Experteer Overview As a Principal Machine Learning Engineer, you design, build, and scale ML and generative AI systems powering real-world products. You collaborate with AI and business teams to translate advanced ML/LLM capabilities into production-grade solutions across GCP, AWS, and Azure. The role blends ML, software engineering, and MLOps to deliver robust, scalable cloud-native systems. You will work on end-to-end pipelines and multi-cloud deployments that drive measurable impact, shaping how AI enables business outcomes. Compensation / Benefits * Design, develop, and deploy ML and LLM-based solutions for production use cases * Collaborate with Generative AI Center of Excellence leaders and stakeholders to evaluate buy vs. build for generative AI * Develop end-to-end ML pipelines (data ingestion, feature engineering, training, evaluation, deployment, monitoring) * Architect and implement LLM-powered systems across multiple clouds into a unified solution * Optimize ML workflows for performance, scalability, reliability, and cost in cloud environments * Implement and maintain MLOps best practices (CI/CD, model versioning, experiment tracking, retraining) * Work with PyTorch and TensorFlow for model development * Containerize ML services and deploy via Docker, Kubernetes, App Engine, or VMs * Apply NLP fundamentals (transformers, attention, embeddings, preprocessing) * Deploy and manage models in production, conduct A/B testing, measure performance with statistics * Develop features, run experiments, translate insights into improvements * Build and deploy classical ML models and NLP/vision applications (sentiment, summarization, Q&A, chatbots, CV tasks) Tasks * PhD with 5+ years, Master with 6+ years, or Bachelor with 7+ years in ML/CS/Data Science or related field * Strong Python proficiency for ML and production systems * Solid software engineering fundamentals, system design, and design patterns * Hands-on experience with at least one major cloud platform (GCP, Azure, AWS) * Experience building and deploying production-grade ML systems * Strong communication skills to explain technical concepts to diverse stakeholders * Excellent time management, collaboration, and organizational skills Key requirements * Employee Assistance Program * Health, life, disability insurance * Retirement plans * Paid Time Off (PTO) and holidays * Equity may be offered * Paid vacations and holidays

for performance, scalability, reliability, and cost in cloud environments * Implement and maintain MLOps best practices (CI/CD, model versioning, experiment tracking, retraining) * Work with PyTorch and TensorFlow for model development * Containerize ML services and deploy via Docker, Kubernetes, App Engine, or VMs * Apply NLP fundamentals (transformers, attention, embeddings, preprocessing) * Deploy and manage models in production, conduct A/B testing, measure performance with statistics * Develop features, run experiments, translate insights into improvements * Build and deploy classical ML models and NLP/vision applications (sentiment, summarization, Q&A, chatbots, CV tasks) Tasks * PhD with 5+ years, Master with 6+ years, or Bachelor with 7+ years in ML/CS/Data Science or related field * Strong Python proficiency for ML and production systems * Solid software engineering fundamentals, system design, and design patterns * Hands-on experience with at least one major cloud platform (GCP, Azure, AWS) * Experience building and deploying production-grade ML systems * Strong communication skills to explain technical concepts to diverse stakeholders * Excellent time management, collaboration, and organizational skills Key requirements * Employee Assistance Program * Health, life, disability insurance * Retirement plans * Paid Time Off (PTO) and holidays * Equity may be offered * Paid vacations and holidays