Principal Machine Learning Engineer

• Design, develop, and deploy machine learning and Large Language Model (LLM)–based solutions for production use cases

• Collaborate with Generative AI Center of Excellence leaders and business stakeholders to evaluate buy vs. build decisions for generative AI applications

• Build and integrate agent-based workflows using platforms such as Google Agentspace, Microsoft Copilot, and Salesforce Agentforce

• Develop end-to-end ML pipelines, covering data ingestion, feature engineering, model training, evaluation, deployment, and monitoring

• Architect and implement LLM-powered systems that integrate agents and services across multiple cloud platforms into a unified solution

• Optimize ML workflows for performance, scalability, reliability, and cost efficiency in cloud environments (GCP, Azure, AWS)

• Implement and maintain MLOps best practices, including CI/CD, model versioning, experiment tracking, and automated retraining

• Work extensively with deep learning frameworks such as PyTorch and TensorFlow

• Containerize ML services and deploy them using Docker, Kubernetes, App Engine, or virtual machines

• Apply strong knowledge of NLP fundamentals, including transformers, attention mechanisms, embeddings, and text preprocessing

• Deploy and manage models in production, conduct A/B testing, and measure performance improvements using statistical methods

• Develop features, run experiments, analyze results, and translate insights into actionable improvements

• (Good to have) Build and deploy classical ML models (regression, classification, clustering), NLP applications (sentiment analysis, summarization, Q&A, chatbots, information retrieval), and computer vision solutions (image classification, object detection, segmentation using models such as YOLOv7, DDRNet, RFTM with datasets like COCO and Cityscapes)

Qualifications

• PhD with 5+ years, Master’s with 6+ years, or Bachelor’s with 7+ years of experience in Machine Learning, Computer Science, Data Science, or a related field

• Strong proficiency in Python for machine learning and production systems

• Solid understanding of software engineering fundamentals, system design, and design patterns

• Hands-on experience with at least one major cloud platform (GCP, Azure, or AWS)

• Experience building and deploying production-grade ML systems

• Strong communication skills with the ability to explain technical concepts and results to both technical and non-technical stakeholders

• Excellent time management, collaboration, and organizational skills