IT Supply Chain Analyst

Ford Motor Company; Chennai; On-site; INR 900,000 - 2,100,000; Full time

Job summary

Lincoln Motor Company seeks an AI automation engineer to streamline operational processes and build AI-assisted workflows. You will design, test, and maintain automations for health checks, data validation, and incident summaries, while enabling跨-team collaboration through APIs and secure workflows.

You will apply agentic AI patterns, implement safeguards, and use predictive analytics to anticipate issues and trigger automated actions.

Qualifications

• Bachelor’s or master’s degree in computer science, engineering, maths or science.
• Experience with NLP/LLM courses or open competitions is welcomed.
• Strong Python and GenAI / RAG concepts knowledge.

Responsibilities

• Identify and prioritize repetitive production-support tasks that can be automated.
• Design, develop, test, and maintain operational automations (health checks, data validation, ticket enrichment, incident summaries, 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 with clear permissions, approvals and fallback handling.
• Use predictive analytics and anomaly detection to identify patterns and trigger alerts or automated actions.
• Integrate automation with monitoring, ticketing, knowledge-management, and Supply Chain systems via APIs.
• Implement safeguards and quality controls including testing, access management, audit logging, and human review.
• Measure automation outcomes and share reusable patterns with the team.

Skills

Python

AI/GenAI workflows

REST APIs

Git

Docker

Kubernetes

SQL

Pandas/Scikit-learn

Testing (Pytest)

Code quality (Ruff)

Education

Bachelor’s or Master’s degree in Computer Science, Engineering, Maths or Science

Tools

Docker

Kubernetes

Terraform

CI/CD pipelines

Job description

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.