Description

Key Skills: Agentic AI, AI Ops, Machine Learning, Python, Kubernetes, LLM Systems, Cloud Native, System Design, CI/CD, Observability

Good to Have Skills: Experience with workflow orchestration frameworks like LangGraph and AutoGen, distributed systems architecture, telemetry systems, SRE practices, reliability engineering principles, multi-step workflows, tool integration, memory and context handling, scalability and performance optimization, predictive analytics, automated remediation capabilities.

Roles & Responsibilities:

• Design and implement agentic AI systems that plan, reason, and execute multi-step workflows across platform and portal ecosystems.
• Build and deploy AI-driven automation for operations including incident detection, triage, remediation, and monitoring.
• Develop and productionize LLM-based applications and intelligent workflows integrated with APIs, tools, and enterprise systems.
• Integrate AI solutions with platform infrastructure including Kubernetes, CI/CD, observability, and telemetry pipelines.
• Drive proactive operations through predictive analytics and automated remediation capabilities for improved system reliability.
• Establish scalable patterns for AI lifecycle covering design, deployment, monitoring, and optimization phases.
• Collaborate with platform, DevOps, SRE, and application teams to embed AI into engineering workflows effectively.
• Provide technical leadership, mentor engineers, and define best practices for AI engineering and automation.

Experience Required: 12+ years of overall industry experience with experience building and deploying AI/ML or Generative AI solutions in production, strong software engineering fundamentals, and experience with cloud-native and distributed systems.

Education: Bachelor's degree in Computer Science, Engineering, or related field

Key Skills

Education

Any Graduate