Advanced AI Operations Professional

3 days ago

Bengaluru, Karnataka, India beBeeAiopter Full time ₹ 1,50,00,000 - ₹ 2,00,00,000

At the forefront of innovation, we seek a visionary AI Ops Engineer to spearhead the integration of DevOps and AI. As a key member of our team, you will be responsible for designing and implementing cutting-edge AIOps platforms that harness the power of automation, analytics, and artificial intelligence to propel our systems to unprecedented heights.

Responsibilities:
  • Design and develop comprehensive AIOps strategies that align with our business objectives.
  • Implement and manage scalable AIOps platforms, leveraging machine learning and automation to drive intelligent monitoring, alerting, and incident response.
  • Collaborate with cross-functional teams to integrate AI-driven insights into our DevOps workflows, ensuring seamless communication and optimized system performance.
  • Automate operational tasks using advanced scripting languages and orchestration tools, reducing downtime and improving overall system reliability.
  • Build and maintain intuitive observability dashboards, utilizing metrics, logs, and traces to provide real-time visibility into system performance.
Requirements:
  • Proven expertise in AIOps, DevOps, and AI, with a strong track record of delivering high-impact projects.
  • Advanced knowledge of cloud platforms (AWS, Azure, GCP) and container orchestration (Kubernetes, Docker).
  • Familiarity with leading observability tools (Prometheus, Grafana, ELK, Splunk, Datadog).
  • Proficiency in scripting languages (Python, Shell) and programming skills.
  • Understanding of machine learning concepts and their application in operations anomaly detection and predictive maintenance.
Tech Stack and Tools:
  • AIOps and DevOps Expertise: Experience implementing and managing AIOps platforms for intelligent monitoring, alerting, and automation.
  • Cloud Proficiency: Hands-on experience with AWS, Azure, or GCP for infrastructure and deployment.
  • Containerization and Orchestration: Strong understanding of Docker and Kubernetes for scalable deployments.
  • Observability Tools: Proficiency with Prometheus, Grafana, ELK/EFK Stack, Splunk, or Datadog.
  • Automation and Scripting: Skilled in Python, Shell, or similar scripting for automating operational workflows.
Nice-to-Have:
  • GenAI Integration: Familiarity with generative AI and its application in operational insights and incident response.
  • MLOps Knowledge: Understanding of ML model lifecycles (CI/CD for ML, model monitoring).
  • Big Data and Streaming Tools: Familiarity with Kafka, Spark for operational data handling.
  • Incident Management Platforms: Knowledge of PagerDuty, Opsgenie, ServiceNow integrations.

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