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.