Description

job responsibilities include:

Designing and implementing AI-driven tools to enhance real-time monitoring, anomaly detection, and root cause analysis.

Integrating machine learning workflows with telemetry data sources such as Splunk, Dynatrace, and Data Lakes.

Automating root cause analysis (RCA) workflows to reduce Mean Time to Identify (MTTI) and Mean Time to Restore (MTTR).

Building and fine-tuning machine learning models for anomaly detection, platform restoration, and predictive maintenance.

Deploying models in production environments using frameworks like NVIDIA Triton, TensorFlow, or Kubernetes.

Developing efficient data pipelines for ingesting and preprocessing logs, metrics, and traces from observability platforms.

Leveraging vector databases and Retrieval-Augmented Generation (RAG) techniques for hybrid search and agentic workflows.

Collaborating with cross-functional teams to identify AI use cases and translate them into scalable, production-ready solutions.

Ensuring continuous integration and delivery (CI/CD) for smooth deployment cycles and modern DevOps practices.

Desired Qualifications:

A Bachelor's or master’s degree in computer science, Data Science, Engineering, or a related field.

A minimum of 3+ years of experience in machine learning, AI model development, and production deployment.

Strong experience with machine learning frameworks such as TensorFlow, PyTorch, and LangChain.

Expertise in AI/ML tools and platforms for anomaly detection, predictive maintenance, and NLP-based tools

Key Skills

Education

Any Graduate