Introduction
At IBM, work is more than a job – it’s a calling: To build. To design. To code. To consult. To think along with clients and sell. To make markets. To invent. To collaborate. Not just to do something better, but to attempt things you’ve never thought possible. Are you ready to lead in this new era of technology and solve some of the world’s most challenging problems? If so, lets talk.

Your Role and Responsibilities
What you will do (Roles & Responsibilities):

• Utilize expertise in AI/ML and Data Science to develop and deploy AI models in production environments, ensuring scalability, reliability, and efficiency.
• Implement and optimize machine learning algorithms, neural networks, and statistical modeling techniques to solve complex problems.
• Hands-on experience in developing and deploying large language models (LLMs) in production environments, with a good understanding of distributed systems, microservice architecture, and REST APIs.
• Collaborate with cross-functional teams to integrate MLOps pipelines with CI/CD tools for continuous integration and deployment.
• Stay updated with the latest advancements in AI/ML technologies and contribute to the development and improvement of AI frameworks and libraries.
• Communicate technical concepts effectively to non-technical stakeholders, demonstrating excellent communication and interpersonal skills.
• Ensure compliance with industry best practices and standards in AI engineering, maintaining high standards of code quality, performance, and security.
• Experience in using container orchestration platforms such as Kubernetes to deploy and manage machine learning models in production environments.

Required Technical and Professional Expertise
1.Programming Proficiency:

• Proficiency in Python, C++.
• Experience with relevant ML libraries (e.g., TensorFlow, PyTorch) for developing production-grade quality products.

2.Data Handling Skills:

• Skilled in integrating, cleansing, and shaping data.
• Expertise in various databases including open-source databases like MongoDB, CouchDB, CockroachDB.

3.DevOps Experience:

• Experienced in DevOps practices.

• Skills in Git, CI/CD pipelines (Jenkins, Travis CI, GitLab CI), and containerization (Docker, Kubernetes).

4.Open-Source Contribution:

• Open-source Contribution is a plus.
• Experience in contributing to open-source AI projects or utilizing open-source AI frameworks.

5.Problem-Solving Skills:

• Strong problem-solving and analytical skills.
• Experience in optimizing AI algorithms for performance and scalability.

6.AI Compiler/Runtime Skills:

• AI compiler/runtime skills would be a plus.

7.Agile Methodologies:

• Familiarity with Agile methodologies.
• Experience in Agile development of AI-based solutions.
• Ensuring efficient project delivery through iterative development processes

Preferred Technical and Professional Expertise

• Proven ability to implement and optimize machine learning algorithms, neural networks, and statistical modeling techniques to solve complex problems effectively.
• Proficiency in distributed systems, microservice architecture, and REST APIs.
• Experience in collaborating with cross-functional teams to integrate MLOps pipelines with CI/CD tools for continuous integration and deployment, ensuring seamless integration of AI/ML models into production workflows.
• Demonstrated commitment to staying updated with the latest advancements in AI/ML technologies.
• Proven ability to contribute to the development and improvement of AI frameworks and libraries.
• Strong communication skills with the ability to communicate technical concepts effectively to non-technical stakeholders.
• Demonstrated excellence in interpersonal skills, fostering collaboration across diverse teams.
• Proven track record of ensuring compliance with industry best practices and standards in AI engineering.
• Maintained high standards of code quality, performance, and security in AI projects.
• Experience in using container orchestration platforms such as Kubernetes to deploy and manage machine learning models in production environments, ensuring efficient scalability and management of AI infrastructure.