In person interview on 18th July - Saturday:

Technical Requirements:

- Minimum 7-8 years of experience in Data Science and Machine Learning.

- In depth knowledge of machine learning, deep learning, and generative AI techniques.

- Proficiency in programming languages such as Python and frameworks like TensorFlow or PyTorch.

- Strong understanding of NLP techniques and frameworks such as BERT, GPT, or Transformer models.

- Experience with cloud platforms such as Azure, AWS, or GCP and deploying AI solutions in a cloud environment.

- Expertise in data engineering including data curation, cleaning, and preprocessing.

- Knowledge of trusted AI practices like fairness, transparency, and accountability in AI models and systems.

- Strong collaboration with engineering teams to ensure seamless integration and deployment of AI models.

- Excellent problem-solving and analytical skills with the ability to translate business requirements into technical solutions.

- Strong communication and interpersonal skills with the ability to collaborate effectively with stakeholders at various levels.

- Understanding of data privacy, security, and ethical considerations in AI applications.

- Track record of driving innovation and staying updated with the latest AI research and advancements.

Good to Have Skills:

- Knowledge and/or hands-on experience in Cyber domain AI applications.

- Vulnerability Management tools such as AquaSec, Qualys, Wiz, Check Marx, and MS Defender.

- Knowledge of secure software development lifecycle (SSDLC) processes.

- Apply trusted AI practices to ensure fairness, transparency, and accountability in AI models and systems.

- Knowledge on DevOps and MLOps practices covering continuous integration, deployment, and monitoring of AI models.

- Implement CI/CD pipelines for streamlined model deployment and scaling processes.

- Utilize tools such as Docker, Kubernetes, and Git to build and manage AI pipelines.

- Implement monitoring and logging tools to ensure AI model performance and reliability.