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ML OPS Engineer at Ford Motor Company
Chennai, tamil nadu, India -
Full Time

Start Date

Immediate

Expiry Date

10 Aug, 26

Salary

0.0

Posted On

12 May, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

No

Skills

MLOps, DevOps, CI/CD, Tekton, GCP, Vertex AI, Cloud Run, GKE, BigQuery, Dynatrace, Observability, Machine Learning Pipelines, Distributed Tracing, Software Engineering, Infrastructure Management, Automation

Industry

Motor Vehicle Manufacturing

Description
In this role, you will be part of the analytics team, playing a critical role in the industrialization of data science to drive significant business impact. As an MLOps Engineer, you will lead the large-scale deployment and maintenance of complex machine learning pipelines. You will build and manage the underlying infrastructure that allows AI models to move seamlessly from research to production, ensuring that these systems are not only high-performing but also safe, observable, and robust. You will be responsible for architecting operational workflows that bridge the gap between data science development and enterprise-grade IT operations, ensuring that every model is backed by rigorous automation and monitoring. Your responsibilities will include designing and implementing automated CI/CD pipelines using Tekton to orchestrate the end-to-end lifecycle of ML models. You will be a champion of operational excellence, implementing advanced observability and traceability frameworks to monitor model health and system performance in real-time. You will utilize Dynatrace and centralized logging solutions to build comprehensive monitoring pipelines that track latency, resource utilization, and system errors. Additionally, you will be responsible for maintaining the stability of large-scale deployments, ensuring that all AI assets integrate seamlessly with the internal ecosystem via standardized protocols. Development and deployment will occur exclusively within the GCP ecosystem, utilizing Vertex AI, Cloud Run, GKE, and BigQuery.
Responsibilities
Lead the large-scale deployment and maintenance of complex machine learning pipelines within the GCP ecosystem. Architect operational workflows and implement automated CI/CD pipelines using Tekton to bridge the gap between data science and IT operations.