eTeam
Role name: Principal ML engineer
Work Location: Bellevue, US (onsite)
Contract
Role and responsibilities: 8-12+ 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.
Role and responsibilities: 8-12+ 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.