Oracle

Senior Software Developer

Posted On

June 25, 2025

Location

Bengaluru, Karnataka

Experience Level

Professional

Job Type

Permanent

Apply By

Dec. 22, 2025

Job Description

Oracle Database Performance Specialist - ( AI/ML Engineer) About Oracle – Customer Success Services

Oracle is the only provider of fully integrated technology solutions that span both infrastructure and applications. Today’s businesses, however, need more than just the best technology solutions. They need a strategic partner to support sustained business growth. Oracle Customer Success Services (CSS) was created to help ensure customer’s ongoing success with our technology. CSS is completely integrated with Oracle’s product development teams to help maximize the value of customer’s cloud investment.

Job Responsibilities:

As CSS AI Engineer, Work directly with customers and have a solid understanding of the application development and support processes. 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.

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.

Mandatory Skills:

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.

If you have the above skills, take up the below list of self-test questions to know if you qualify to apply.

Self-Test Questions:

Is supervised learning used when the data is labelled?

Answer : Yes, Supervised learning requires a labelled dataset, where each training example has a corresponding output label.

Can overfitting occur when a model is too complex?

Answer : Yes, Overfitting happens when a model is excessively complex, capturing noise in the training data rather than generalizing to new data.

Is decision tree a type of supervised learning algorithm?

Answer : Yes, A decision tree is a supervised learning algorithm used for both classification and regression tasks.

Can gradient descent be used for optimization in neural networks?

Answer : Yes, Gradient descent is commonly used to optimize the weights in neural networks by minimizing the loss function.

Is normalization used to scale features between 0 and 1?

Answer: Yes, Normalization scales the data to a specific range, often between 0 and 1, to improve the performance of machine learning algorithms.

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