This position is for a AI Engineer who is well versed with System skills and strong back end development knowledge in C++. The engineer will get to lead in building AI libraries, frameworks and solutions for IBM Z systems.
1. Lead Development and deployment of AI frameworks, leveraging deep expertise in AI/ML and Data Science to ensure scalability, reliability, and efficiency.
2. Direct the implementation and optimization of machine learning algorithms, neural networks, and statistical modeling techniques, personally driving solutions for complex problems.
3. Personally oversee the development and deployment of AI solutions in production environments.
4. 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.
5. 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.
6. Effectively communicate technical concepts to non-technical stakeholders, showcasing excellent communication and interpersonal skills while leading discussions and decision-making processes.
7. Uphold industry best practices and standards in AI engineering , maintaining unwavering standards of code quality, performance, and security throughout the development lifecycle.
8. 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.
1. AI product development Leadership:
- Deep experience in demonstrating coding skills, teaming capabilities, and end-to-end understanding of Enterprise AI product.
- Deep background in machine learning, deep learning.
- Expertise with product design, design principles and integration with various other enterprise products.
- Strong skills in programing with C++, Python
2. Model Development Expertise:
- Hands-on expertise with traditional machine learning, transformer-based and diffuser-based models (e.g., BERT, GPT, T5, Llama, Stable diffusion), showcasing mastery in model development and optimization.
- Desirable experience in rigorously testing AI algorithms and models, ensuring robustness and reliability in real-world applications.
3. Traditional AI Methodologies Mastery:
- Demonstrated proficiency in traditional AI methodologies, including mastery of machine learning and deep learning frameworks.
- Familiarity with model serving platforms such as Triton inference server, TGIS and vLLM, with a track record of leading teams in effectively deploying models in production environments.
- Proficient in developing optimal data pipeline architectures for AI applications, taking ownership of designing scalable and efficient solutions.
4. Development Ownership:
- Proficient in backend C++, with hands-on experience integrating AI technology into full-stack projects.
- Demonstrated understanding of the integration of AI tech into complex full-stack applications.
5. Problem-Solving and Optimization Skills:
- Demonstrated strength in problem-solving and analytical skills, with a track record of optimizing AI algorithms for performance and scalability.
- Leadership in driving continuous improvement initiatives, enhancing the efficiency and effectiveness of AI solutions.
1. Knowledge in AI/ML and Data Science:
- Over 13 years of demonstrated leadership in AI/ML and Data Science, driving the development and deployment of AI models in production environments with a focus on scalability, reliability, and efficiency.
- Ownership mentality, ensuring tasks are driven to completion with precision and attention to detail.
2. Algorithm Implementation Mastery and Optimization:
- Proven track record of hands-on implementation and optimization of machine learning algorithms, neural networks, and statistical modeling techniques, showcasing expertise in solving complex problems effectively.
3. Development of Large Language Models (LLMs):
- Hands-on experience in the development and deployment of large language models (LLMs) in production environments, demonstrating proficiency in distributed systems, microservice architecture, and REST APIs.
- Understanding the end-to-end development process of LLMs, from ideation to deployment, ensuring seamless integration into production workflows.
4. Commitment to Continuous Learning and Contribution:
- Demonstrated dedication to continuous learning and staying updated with the latest advancements in AI/ML technologies.
- Proven ability to contribute actively to the development and improvement of AI frameworks and libraries.
5. Effective Communication and Collaboration:
- Strong communication skills, with the ability to effectively convey technical concepts to non-technical stakeholders.
- Excellence in interpersonal skills, fostering collaboration and teamwork across diverse teams to drive projects to successful completion.