Principal Machine Learning Engineer

Overview

Principal Machine Learning Engineer role leading development and implementation of advanced machine learning models, mentoring junior team members, and driving strategic AI and data science initiatives, reporting to the Director, Software Development Engineering in the Exposure Management & Security Operations department.

What you'll do

• Architect, implement, and evaluate advanced machine learning models and innovative technical solutions directly aligned with strategic business objectives
• Guide and support data science team members through hands-on project direction, technical best practices, and structured professional development
• Partner with cross-functional teams to define project roadmaps and translate complex technical concepts into actionable insights that drive data-informed decision-making
• Champion organization-wide strategic initiatives, proactively identifying high-impact opportunities for process optimization and technological innovation
• Monitor emerging advancements in machine learning and data science, translating state-of-the-art research into practical, high-value applications

What you'll need

• Foundational understanding of AI/ML technologies and experience leveraging, securing, or positioning AI-driven solutions to optimize outcomes within your functional domain
• 12+ years of proven success as a Machine Learning Engineer or Data Scientist, leading high-impact projects that leverage ML/AI to solve real-world business problems at scale
• Deep expertise in Python (pandas, scikit-learn, PyTorch) and SQL, backed by extensive hands-on experience in feature engineering, rigorous model evaluation, and comprehensive error analysis
• In-depth knowledge of Large Language Models (LLMs) and their practical applications, coupled with a proven agility to rapidly learn, evaluate, and adopt emerging technologies
• Outstanding interpersonal and technical communication skills, enabling seamless collaboration and the ability to articulate complex technical concepts clearly to diverse stakeholders

Nice to have

• Deep expertise in fine-tuning Large Language Models, advanced prompt engineering, Graph Neural Networks, Knowledge Graphs, and unsupervised clustering and evaluation
• Strong command of operating systems and distributed architectures, with hands-on proficiency in (Py)Spark, major public cloud platforms (AWS, Azure, GCP), and ML automation tooling such as Kubeflow
• Demonstrated track record of technical innovation supported by relevant research publications or patents, alongside a solid foundational understanding of networking and network security concepts

Details

• Location: Bangalore, India
• Work mode: Hybrid