AI ML Engineer

Job Description

Job Description: As an AI Engineer, you will work closely with cross-functional teams to design, build, and deploy AI solutions in a scalable and efficient manner. You will be responsible for leveraging Docker and Kubernetes for containerization and orchestration, implementing MLOps practices and utilizing your skills in python and machine learning to build and enhance our AI models. Experience in Generative AI is highly desired as we continue to push the boundaries of AI applications.

Responsibilities:

- Design and develop AI/ML models and systems from prototyping to production, while ensuring scalability and efficiency.
- Utilize Docker and Kubernetes for containerization, deployment and orchestration of AI models and services in production environments.
- Implement and manage end-to-end MLOps pipelines to automate model training, testing, deployment and monitoring.
- Build robust, reusable, and scalable Python code for AI model development and deployment.
- Work on the integration and deployment of generative AI models and systems for innovative applications.
- Troubleshoot, optimize and maintain production-level AI models and services.
- Stay up-to-date with the latest trends and advancements in AI, machine learning and MLOps technologies.

Requirements:

- Bachelor's or Master's degree in Computer Science, Engineering or related field.
- Proven experience (3+ years) in AI/ML engineering with a strong focus on Docker, Kubernetes, and MLOps.
- Expertise in Python and frameworks/libraries such as TensorFlow, PyTorch, Scikit-learn or similar.
- Solid understanding of machine learning algorithms and techniques, particularly in deep learning and natural language processing (NLP).
- Hands-on experience deploying and managing AI models in production using Docker and Kubernetes.
- Knowledge of cloud platforms (OCI, AWS, Azure, GCP) and how they relate to containerized AI deployments.
- Experience with CI/CD pipelines and version control (Git).
- Strong understanding of generative AI models such as GANs, VAEs or transformers.
- Familiarity with monitoring, logging, and debugging AI/ML systems in production environments.
- Excellent communication and teamwork skills, with the ability to collaborate with diverse teams.

Preferred Qualifications:

- Experience with large-scale AI model deployments.
- Knowledge of additional languages such as Java, Go or C++.
- Familiarity with data engineering tools and frameworks (Apache Kafka, Spark etc.).
- Exposure to AI ethics and governance.

Career Level - IC3

Job Description: As an AI Engineer, you will work closely with cross-functional teams to design, build, and deploy AI solutions in a scalable and efficient manner. You will be responsible for leveraging Docker and Kubernetes for containerization and orchestration, implementing MLOps practices and utilizing your skills in python and machine learning to build and enhance our AI models. Experience in Generative AI is highly desired as we continue to push the boundaries of AI applications.

Responsibilities:

- Design and develop AI/ML models and systems from prototyping to production, while ensuring scalability and efficiency.
- Utilize Docker and Kubernetes for containerization, deployment and orchestration of AI models and services in production environments.
- Implement and manage end-to-end MLOps pipelines to automate model training, testing, deployment and monitoring.
- Build robust, reusable, and scalable Python code for AI model development and deployment.
- Work on the integration and deployment of generative AI models and systems for innovative applications.
- Troubleshoot, optimize and maintain production-level AI models and services.
- Stay up-to-date with the latest trends and advancements in AI, machine learning and MLOps technologies.

Requirements:

- Bachelor's or Master's degree in Computer Science, Engineering or related field.
- Proven experience (3+ years) in AI/ML engineering with a strong focus on Docker, Kubernetes, and MLOps.
- Expertise in Python and frameworks/libraries such as TensorFlow, PyTorch, Scikit-learn or similar.
- Solid understanding of machine learning algorithms and techniques, particularly in deep learning and natural language processing (NLP).
- Hands-on experience deploying and managing AI models in production using Docker and Kubernetes.
- Knowledge of cloud platforms (OCI, AWS, Azure, GCP) and how they relate to containerized AI deployments.
- Experience with CI/CD pipelines and version control (Git).
- Strong understanding of generative AI models such as GANs, VAEs or transformers.
- Familiarity with monitoring, logging, and debugging AI/ML systems in production environments.
- Excellent communication and teamwork skills, with the ability to collaborate with diverse teams.

Preferred Qualifications:

- Experience with large-scale AI model deployments.
- Knowledge of additional languages such as Java, Go or C++.
- Familiarity with data engineering tools and frameworks (Apache Kafka, Spark etc.).
- Exposure to AI ethics and governance.

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