Company Name

Ford ( Chennai )

Job Description

• • Machine Learning Solution Development:
  • Design, develop, and implement advanced machine learning models (supervised and unsupervised) to solve complex IT Operations problems, including Event Correlation, Anomaly Detection, Root Cause Analysis, Predictive Analytics, and Auto-Remediation.
  • Leverage structured and unstructured datasets, performing extensive feature engineering and data preprocessing to optimize model performance.
  • Apply strong statistical modeling, hypothesis testing, and experimental design principles to ensure rigorous model validation and reliable insights.
  • AI/ML Product & Platform Development:
  • Lead the end-to-end development of Data Science products, from conceptualization and prototyping to deployment and maintenance.
  • Develop and deploy AI Agents for automating workflows in IT operations, particularly within Networks and CyberSecurity domains.
  • Implement RAG (Retrieval Augmented Generation) based retrieval frameworks for state-of-the-art models to enhance contextual understanding and response generation.
  • Adopt AI to detect and redact sensitive data in logs, and implement central data tagging for all logs to improve AI Model performance and governance.
  • MLOps & Deployment:
  • Drive the operationalization of machine learning models through robust MLOps/LLMOps practices, ensuring scalability, reliability, and maintainability.
  • Implement models as a service via APIs, utilizing containerization technologies (Docker, Kubernetes) for efficient deployment and management.
  • Design, build, and automate resilient Data Pipelines in cloud environments (GCP/Azure) using AI Agents and relevant cloud services.
  • Cloud & DevOps Integration:
  • Integrate data science solutions with existing IT infrastructure and AIOps platforms (e.g., IBM Cloud Paks, Moogsoft, BigPanda, Dynatrace).
  • Enable and optimize AIOps features within Data Analytics tools, Monitoring tools, or dedicated AIOps platforms.
  • Champion DevOps practices, including CI/CD pipelines (Jenkins, GitLab CI, GitHub Actions), infrastructure-as-code (Terraform, Ansible, CloudFormation), and automation to streamline development and deployment workflows.
  • Performance & Reliability:
  • Monitor and optimize platform performance, ensuring systems are running efficiently and meeting defined Service Level Agreements (SLAs).
  • Lead incident management efforts related to data science systems and implement continuous improvements to enhance reliability and resilience.
  • Leadership & Collaboration:
  • Translate complex business problems into data science solutions, understanding their strategic implications and potential business value.
  • Collaborate effectively with cross-functional teams including engineering, product management, and operations to define project scope, requirements, and success metrics.
  • Mentor junior data scientists and engineers, fostering a culture of technical excellence, continuous learning, and innovation.
  • Clearly articulate complex technical concepts, findings, and recommendations to both technical and non-technical audiences, influencing decision-making and driving actionable outcomes.
  • Best Practices:
  • Uphold best engineering practices, including rigorous code reviews, comprehensive testing, and thorough documentation.
  • Maintain a strong focus on building maintainable, scalable, and secure systems.

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