Lead Software Engineer- ML

We are seeking a skilled and forward-thinking Lead Software Engineer specializing in Machine Learning with over 10 years of experience to spearhead the conceptualization, development, and implementation of cutting-edge machine learning solutions. This pivotal role necessitates robust leadership qualities, profound technical acumen, and a demonstrated track record in steering teams towards resolving intricate, large-scale challenges utilizing state-of-the-art ML technologies. In this leadership capacity, you will be responsible for mentoring teams, formulating technical roadmaps, and fostering collaboration across various departments to synchronize machine learning endeavors with business objectives. Your responsibilities will include defining and orchestrating the strategy and trajectory for ML systems and applications, as well as architecting and supervising the construction of adaptable machine learning systems and infrastructure. You will drive the creation and execution of sophisticated ML models and algorithms to tackle complex business issues, collaborate with multifaceted teams to discern ML use cases and prerequisites, and provide guidance to junior and mid-level engineers on optimal practices for ML development and deployment. It will also be imperative to oversee the performance enhancement of machine learning systems in operational settings, ensure adherence to industry standards and best practices in model development, data governance, and MLOps, and spearhead research endeavors to explore emerging ML methodologies and seamlessly integrate them into the organization's solutions. The ideal candidate should hold a Bachelor's or Master's degree in Computer Science, Machine Learning, Artificial Intelligence, or a related field, with a Ph.D. being an advantageous asset. Additionally, you should possess a minimum of 10 years of software engineering experience, with at least 5 years dedicated to machine learning, and exhibit proficiency in ML frameworks and libraries like TensorFlow, PyTorch, and scikit-learn. A strong grasp of designing and constructing 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, and expertise in deploying and managing ML models in production environments using MLOps tools like MLflow or Kubeflow are essential technical skills that you should bring to the table. Moreover, a deep understanding of algorithms, data structures, system design, containerization (Docker), orchestration (Kubernetes), and exceptional problem-solving capabilities are highly valued attributes for this role. Your soft skills should include robust leadership and decision-making prowess, exceptional problem-solving and analytical thinking, excellent communication aptitude to convey technical concepts to diverse audiences, and the ability to cultivate collaboration and drive innovation across teams. Preferred qualifications include a Master's degree in Computer Science, Information Technology, or a related field, familiarity with advanced techniques like generative AI, reinforcement learning, or federated learning, experience in constructing and managing real-time data processing pipelines, knowledge of data security and privacy best practices, and a track record of publications or patents in the domain of machine learning or artificial intelligence. Key Performance Indicators for this role encompass the successful delivery of scalable, high-impact ML solutions in alignment with business objectives, effective mentorship and upskilling of team members, continuous enhancement of ML system performance and reliability, and driving innovation and adoption of emerging ML techniques to sustain a competitive advantage.,