Application Production Support Engineer - Generative AI Tools- C12 - PUNE

Provide front-line technical support: Diagnose and resolve production issues related to our generative AI applications, including performance bottlenecks, API errors, data inconsistencies, and infrastructure problems.

Monitor application health: Utilize monitoring tools and dashboards to track key metrics, identify anomalies, and proactively address potential issues before they impact users.

Incident Management: Follow established incident management procedures to document, escalate, and resolve production incidents, ensuring timely communication with stakeholders.

Collaborate with engineering teams: Work closely with development and infrastructure teams to identify the root cause of issues, implement fixes, and prevent future occurrences.

Develop and maintain documentation: Create and update technical documentation, including runbooks, knowledge base articles, and troubleshooting guides.

Automate support tasks: Identify opportunities to automate repetitive tasks and improve support efficiency through scripting and tooling.

Participate in on-call rotation: Provide on-call support on a rotational basis to ensure 24/7 coverage for critical applications.

Continuous Improvement: Contribute to the continuous improvement of our support processes and tools by identifying areas for optimization and implementing best practices.

Qualifications:

Bachelor's degree in Computer Science, Engineering, or a related field.

8-13 years of experience in application production support or a related role.

Strong understanding of software development lifecycle and DevOps principles.

Experience supporting cloud-based applications, preferably on AWS, Azure, or GCP.

Proficiency in at least one scripting language (e.g., Python, Bash).

Familiarity with monitoring tools (e.g., Datadog, Prometheus, Grafana).

Excellent problem-solving and analytical skills.

Strong communication and collaboration skills.

A passion for AI and machine learning.

Bonus Points:

Experience with generative AI models and algorithms (e.g., GANs, VAEs, Transformers).

Knowledge of MLOps principles and practices.

Experience with containerization technologies (e.g., Docker, Kubernetes).

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