Description
- Demonstrated leadership with over 12 years of experience in Data Science, specializing in machine learning, deep learning, and natural language processing. Proven ability to lead teams and take ownership of end-to-end activities.
- Strong grounding in traditional AI methodologies, covering machine learning and deep learning frameworks, with the capacity to guide and mentor team members.
- Proficient in utilizing model serving platforms such as TGIS and vLLM, with a knack for overseeing project implementations from conception to delivery.
- Preferred expertise in transformer-based and diffuser-based models (e.g., BERT, GPT, T5, Llama, Stable diffusion), coupled with hands-on involvement in testing AI algorithms and models.
- Mastery of Python, C++, Go, Java, and relevant ML libraries (e.g., TensorFlow, PyTorch) for developing top-tier, production-grade products. Proven ability to lead technical teams in the implementation of complex solutions.
- Skillful in full-stack development, encompassing frontend (HTML, CSS, JavaScript) and backend (Django, Flask, Spring Boot), with demonstrated experience in integrating AI technology into full-stack projects. Proficient in data integration, cleansing, and shaping, with expertise in various databases including open-source options like MongoDB, CouchDB, CockroachDB.
- Capable of designing optimal data pipeline architectures for AI applications, ensuring adherence to client SLAs, while guiding team members in achieving project milestones.
- Familiarity with Linux platforms and experience in Linux app development, demonstrating leadership in guiding teams through the development lifecycle.
- Proficient in DevOps practices, including Git, CI/CD pipelines (Jenkins, Travis CI, GitLab CI), and containerization (Docker, Kubernetes), with a proven ability to lead teams in adopting efficient development workflows.
- Experience in Generative AI is highly advantageous, with the ability to provide leadership and direction in exploring innovative AI techniques.
- Proficiency in AI compiler/runtime skills, showcasing leadership in driving optimization efforts and performance enhancements.
- Highly valued for open-source contribution, exhibiting leadership by actively participating in and guiding team members through contributions to open-source AI projects and frameworks.
- Strong problem-solving and analytical skills, coupled with the proven ability to lead teams in optimizing AI algorithms for performance and scalability.
- Adept in Agile methodologies, with a track record of leading collaborative teams in Agile development of AI-based solutions.
- Lead the development and deployment of AI models in production environments, leveraging deep expertise in AI/ML and Data Science to ensure scalability, reliability, and efficiency.
- Direct the implementation and optimization of machine learning algorithms, neural networks, and statistical modeling techniques, personally driving solutions for complex problems.
- Personally oversee the development and deployment of large language models (LLMs) in production environments, demonstrating hands-on expertise in distributed systems, microservice architecture, and REST APIs.
- Collaborate closely with cross-functional teams to integrate MLOps pipelines with CI/CD tools for continuous integration and deployment, taking a hands-on approach to ensure seamless integration and efficiency.
- Proactively stay abreast of the latest advancements in AI/ML technologies and actively contribute to the development and improvement of AI frameworks and libraries, leading by example in fostering innovation.
- Effectively communicate technical concepts to non-technical stakeholders, showcasing excellent communication and interpersonal skills while leading discussions and decision-making processes.
- Uphold industry best practices and standards in AI engineering under your direct leadership, maintaining unwavering standards of code quality, performance, and security throughout the development lifecycle.
- Demonstrate leadership in the use of container orchestration platforms such as Kubernetes to deploy and manage machine learning models in production environments, personally overseeing deployment strategies and optimizations.
IBM
Business Development
Business Information Systems
CRM
Data Management
Software
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