IT Supply Chain Analyst

You’ll help reduce repetitive operational work by identifying tasks that can be automated, building safe AI-assisted workflows, and improving how teams detect, diagnose, and prevent incidents. You’ll also help grow the team’s practical AI capabilities.

• Identify and prioritize repetitive production-support tasks that can be simplified or automated, focusing on activities that consume significant team time or create avoidable manual effort.
• Design, develop, test, and maintain operational automations for tasks such as health checks, data validation, ticket enrichment, incident summaries, and approved routine remediation.
• Build AI-assisted workflows for incident triage, knowledge retrieval, runbook guidance, alert correlation, and remediation recommendations.
• Apply agentic AI patterns to automate multi-step operational workflows, using clearly defined permissions, boundaries, approval steps, and fallback behavior.
• Use predictive analytics and anomaly detection to identify operational patterns, anticipate potential issues, and trigger timely alerts or automated actions.
• Integrate automation and AI solutions with approved monitoring, ticketing, knowledge-management, and Supply Chain systems through APIs and other supported interfaces.
• Implement safeguards and quality controls including testing, access management, audit logging, output validation, human review, and safe failure handling.
• Measure and improve automation outcomes by tracking time saved, manual effort reduced, accuracy, adoption, failure rates, and operational impact; document solutions and share reusable patterns with the team.

Education: Bachelor’s or master’s degree in computer science, Engineering, Maths or Science

Performed any modern NLP/LLM courses/open competitions is also welcomed.

Technical Requirements:

1. Agentic AI workflows: Design multi-step AI-agent workflows with tool calling, state management, guardrails, human approvals, and fallback handling.
2. Python scripting: Build and test maintainable Python automations using tools such as pytest and Ruff.
3. Workflow automation: Automate repetitive operations using scripts, REST APIs, webhooks, and workflow orchestration tools.
4. GenAI and RAG: Integrate LLMs with prompts, approved knowledge sources, retrieval-augmented generation, and output evaluation.
5. Data and predictive analytics: Use SQL, pandas, and scikit-learn to analyze operational data and develop anomaly-detection or forecasting solutions.
6. Observability: Use monitoring, logging, and tracing tools to support automation; f
7. Security and quality: Apply least-privilege access, secure credential handling, audit logging, automated testing, and safe failure controls.
8. CI/CD and deployments: Configure Git-based pipelines for automated testing, deployment, approvals, and rollback.
9. Cloud fundamentals: Understand cloud services, identity and access management, networking, storage, compute, and cloud security.
10. Containers and infrastructure: Deploy and troubleshoot containerized workloads using Docker; Kubernetes or Terraform experience is preferred.