Job Description
Role name: Principal ML engineer
Work Location: Bellevue, US (onsite)
Contract
Role and responsibilities:
–+ years in machine learning, AI, or data science roles.
• Proven track record of leading complex ML projects, deploying production-grade AI solutions, and mentoring teams.
• Experience in architecting enterprise-level ML platforms and influencing AI strategy is preferred.
• Expertise in Python, R, Java, or similar languages for ML/AI development.
• Extensive experience with ML frameworks: TensorFlow, PyTorch, Scikit-learn, XGBoost, Keras.
• Strong foundation in statistics, probability, linear algebra, optimization, and data modeling.
• Experience with cloud ML platforms (AWS SageMaker, Azure ML, GCP AI Platform) and large-scale data processing tools (Spark, Hadoop).
• Proficiency in MLOps, CI/CD, containerization (Docker/Kubernetes), and model monitoring.
• Strong leadership, problem-solving, and communication skills; ability to drive strategic initiatives.
Lead the design and architecture of enterprise-level ML solutions and AI platforms.
• Define best practices, frameworks, and technical standards for ML development and deployment.
• Collaborate with stakeholders to align ML initiatives with business goals.
• Drive the development of advanced ML and AI models, including NLP, computer vision, recommendation systems, and reinforcement learning.
• Conduct research on emerging ML/AI technologies, algorithms, and frameworks.
• Experiment with novel approaches to solve high-impact business problems.
• Oversee the deployment of ML models into production environments.
• Implement and maintain end-to-end ML pipelines, CI/CD, and MLOps practices.
• Monitor model performance, manage versioning, and optimize inference efficiency.
• Mentor senior and junior ML engineers, fostering technical growth and knowledge sharing.
• Lead code reviews, design discussions, and architecture evaluations.
• Advocate for a culture of innovation, quality, and excellence in AI/ML development.
• Ensure models and ML solutions adhere to ethical AI principles, privacy, and compliance standards.
• Identify and mitigate risks such as bias, fairness, explainability, and security issues in AI systems.