IT Supply Chain Automation and AI Analyst

Ford Motor; Chennai; On-site; INR 900,000 - 1,500,000; Full time

Job summary

Ford Motor in Chennai seeks an IT Supply Chain Analyst to reduce repetitive work by automating tasks, building AI-assisted workflows, and improving how teams detect, diagnose, and prevent incidents. You’ll develop operational automations for health checks, data validation, ticket enrichment, incident summaries, and runbook guidance.

You will integrate automation with monitoring, ticketing, and SCM systems via APIs, applying guardrails and safe failure handling.

Qualifications

• Bachelor’s or Master’s degree in computer science, engineering, maths or science.
• Familiarity with modern NLP/LLM courses or open competitions is welcomed.

Responsibilities

• Design multi-step AI-agent workflows with tool calling, state management, guardrails, human approvals, and fallback handling.
• Build and test maintainable Python automations using tools such as pytest and Ruff.
• Automate repetitive operations using scripts, REST APIs, webhooks, and workflow orchestration tools.
• Integrate LLMs with prompts, approved knowledge sources, retrieval-augmented generation, and output evaluation.
• Use SQL, pandas, and scikit-learn to analyze operational data and develop anomaly-detection or forecasting solutions.
• Apply observability through monitoring, logging, and tracing to support automation.
• Implement least-privilege access, secure credential handling, audit logging, automated testing, and safe failure controls.
• Configure Git-based pipelines for automated testing, deployment, approvals, and rollback.
• Understand cloud services, IAM, networking, storage, compute, and cloud security.
• Deploy and troubleshoot containerized workloads using Docker; Kubernetes or Terraform experience is preferred.

Skills

Analytical thinking

Problem solving

Communication

Team collaboration

NLP/LLM interest

Education

Bachelor’s/Master’s in CS/Engineering/Math/Science

Tools

Python

pytest

Ruff

REST APIs

webhooks

Git-based pipelines

Docker

Kubernetes

Terraform

SQL

pandas

scikit-learn

Job description

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

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