Lead Software Engineer- ML

You are an accomplished and visionary Lead Software Engineer focused on Machine Learning, responsible for driving the design, development, and deployment of advanced machine learning solutions. Your role entails providing strong leadership, leveraging deep technical expertise, and guiding teams to solve complex, large-scale problems using cutting-edge ML technologies. As a leader, you will mentor teams, define technical roadmaps, and collaborate with various departments to align machine learning initiatives with business objectives. **Roles and Responsibilities:** - Define and lead the strategy and roadmap for ML systems and applications. - Architect and oversee the development of scalable machine learning systems and infrastructure. - Drive the design and implementation of advanced ML models and algorithms to address complex business problems. - Collaborate with cross-functional teams to identify ML use cases and requirements. - Mentor and guide junior and mid-level engineers in best practices for ML development and deployment. - Monitor, evaluate, and improve the performance of machine learning systems in production. - Ensure compliance with industry standards and best practices for model development, data governance, and MLOps. - Lead research initiatives to explore emerging ML techniques and integrate them into the organization's solutions. **Qualifications Required:** - Bachelors or Masters degree in Computer Science, Machine Learning, Artificial Intelligence, or a related field. A Ph.D. is a plus. **Technical Skills Required:** - 10+ years of experience in software engineering, with at least 5 years focusing on machine learning. - Proficiency in ML frameworks and libraries like TensorFlow, PyTorch, and scikit-learn. - Strong expertise in designing and building large-scale, distributed ML systems. - Advanced knowledge of data engineering tools and frameworks, such as Spark, Hadoop, or Kafka. - Hands-on experience with cloud platforms (AWS, GCP, Azure) for ML workloads. - Expertise in deploying and managing ML models in production environments using MLOps tools like MLflow or Kubeflow. - Deep understanding of algorithms, data structures, and system design. - Experience with containerization (Docker) and orchestration (Kubernetes). **Soft Skills Required:** - Strong leadership and decision-making capabilities. - Exceptional problem-solving and analytical thinking. - Excellent communication skills to convey technical concepts effectively. - Ability to foster collaboration and drive innovation across teams. `Note: Preferred Skills/Qualifications and Key Performance Indicators sections have been omitted as per the instructions.`,