AI Engineer

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 6 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.