AI & DevOps Engineer

Shift: Evening Night Shift

Department: IT Infrastructure

Job Summary

We are looking for an AI DevOps Engineer with expertise in AI/ML, LLMs, observability, and automation to develop intelligent solutions that improve system reliability and operational efficiency. The role involves building AI-driven capabilities for monitoring, anomaly detection, predictive analytics, incident response, log analytics, and root cause analysis across enterprise applications, databases, and cloud infrastructure.

Key Responsibilities

• Design, develop, and deploy AI-powered solutions to improve system reliability, automate incident response, and optimize enterprise operations.
  • Build AI-driven capabilities for anomaly detection, event correlation, predictive analytics, root cause analysis, and intelligent operational insights using AI/ML, LLMs, and AI Agents.
  • Design and implement AI-driven automation for infrastructure monitoring, database operations, performance optimization, capacity planning, and operational recommendations.
• Design and optimize enterprise observability solutions using metrics, logs, traces, and events to provide end-to-end visibility across applications and infrastructure.
  • Develop and deploy AI-powered log analytics and operational intelligence solutions using Machine Learning, LLMs, RAG, and AI frameworks.
  • Develop, deploy, and maintain AI/ML models for observability, predictive operations, and intelligent IT automation.

Must-Have Skills

• Develop automation using Python, SQL, Shell scripting, REST APIs, Infrastructure as Code (IaC), and CI/CD practices.
• Enterprise database administration, observability, automation, and Artificial Intelligence (AI) to improve system reliability, operational efficiency, and service availability
• The candidate will develop intelligent AIOps solutions by leveraging AI/ML, Large Language Models (LLMs), and modern observability platforms to enhance IT operations and automation.
• They will drive automation for monitoring, anomaly detection, log analytics, predictive operations, incident response, and root cause analysis across enterprise applications, databases, and cloud infrastructure.

Good-to-Have Skills

• Manage and support enterprise applications, databases (Oracle, PostgreSQL, MySQL, MongoDB), cloud platforms.
• Collaborate with infrastructure, cloud, DevOps, database, security, and application teams to drive operational excellence.
• Evaluate and implement emerging AI technologies, LLMs, MCP, AI Agents, and automation frameworks to continuously improve enterprise IT operations.
• Maintain technical documentation, architecture standards, and operational best practices.

Job Requirements

• Bachelor s degree in computer science, Information Technology, Artificial Intelligence, Data Science, or a related field.
• 3-5 years of experience in AIOps, DevOps or related enterprise operations roles.
• Strong programming skills in Python, SQL, Shell scripting, REST APIs, and automation frameworks.
• Hands-on experience developing and deploying AI/ML solutions for AIOps, observability, automation, or intelligent IT operations.
• Experience building or deploying ML models for anomaly detection, predictive analytics, log analysis, event correlation, and operational intelligence.
• Experience with LLMs, RAG, AI Agents, MCP, LangChain, or similar AI frameworks.
• Knowledge of AI model deployment, MLOps, model lifecycle management, and operational AI pipelines.
• Strong experience with enterprise observability platforms such as Grafana, Prometheus, OpenTelemetry, Elastic/OpenSearch, Splunk, Datadog, or Dynatrace.
• Exposure with Kubernetes, Docker, container platforms, cloud infrastructure (AWS preferred), and on-premises environments.
• Exposure to enterprise databases including Oracle, PostgreSQL, MySQL, and MongoDB.
• Experience with Infrastructure as Code (Terraform, Ansible, or equivalent), CI/CD pipelines, and automation tools.
• Strong analytical, troubleshooting, problem-solving, communication, and collaboration skills.
• Relevant certifications in Cloud, Kubernetes, AI/ML, Observability, Database, or DevOps technologies are an added advantage.