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Medium — specialized AIOps skillset plus known company brand, but seniority and niche reduce broad applicant volume.
High — requires combined AIOps, cloud, DevOps and observability domain expertise.
High — multiple mandatory DevOps, cloud, AI-integration and tooling competencies required.
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Own and lead the design, architecture, and operationalization of AIOps platforms integrating AI/ML models for predictive alerting, anomaly detection, and incident remediation.
Lead technical workstreams and small engineering teams, making architectural decisions, enforcing documentation and coding standards, and driving AI-accelerated development practices with tools like ChatGPT and GitHub Copilot.
Engage with clients throughout the delivery lifecycle including discovery, solution design, and governance, while mentoring engineers and driving automation and process improvements.
Experience Required: Not explicitly mentioned in the JD.
Technical expertise in AI Development (AIOps platform design, AI/ML integration, RAG development, agentic AI workflows).
Strong skills in Cloud platforms (AWS or Azure preferred, GCP considered), DevOps tools (CI/CD pipelines, Infra Automation, containers with Kubernetes), and programming languages (at least one or two among Java, .Net, Python, Go, C#, JavaScript).
Ability to lead architecture design, system specifications, and enforce engineering standards across teams.
Experienced in bridging application development, platform engineering, and AI-driven operations with focus on scalable, maintainable system designs across full software lifecycle.
Capable of hands-on coding balanced with leadership, mentoring, and client engagement in technical delivery and presales activities.
Strong operational mindset with a track record of driving adoption of AI tools and automation, establishing standards and best practices in AI Development Lifecycle and observability strategies.