MLOps Engineer
Confirmed on the employer's own hiring board on Nov 6, 2026. First seen by Alion on Nov 4, 2026. Embitel scores A on the Alion truth index.
About the Company
Embitel Technologies is a Volkswagen AG (VW) Group Company. VW is a German multinational automotive manufacturing corporation headquartered in Wolfsburg, Germany. VW is one of the world’s largest automaker by sales. VW sold 10.9 million vehicles in 2019. The group comprises of 12 brands from seven European Countries: Volkswagen, Audi, Lamborghini, Porsche, Bugatti, Bentley, Ducati (motorcycles), Skoda, SEAT, Scania, MAN and Volkswagen commercial vehicles.
About CARIAD (Car, I Am Digital)
The automotive industry is in disruption. As a company, CARIAD is playing a part in multiple large trends at the same time: electrification, automation, and digitalization. The new name builds on changing customer needs. Automotive customers are demanding more comfort, more safety, more sustainability, and more connectivity. With its software platforms and digital features, CARIAD is developing solutions and products that will turn the car into a smart companion to accompany customers into the future with continuous updates and new digital features, even years later. The new company name reflects this zeitgeist: ‘Car, I Am Digital’. The brand profile can be found on its new website https://cariad.technology.
Currently, around 4,000 employees are working at CARIAD on the digital transformation of the car. CARIAD is working intensively on the software and technology platform for Premium Platform Electric (PPE). The first vehicles based on PPE will be the electric Porsche Macan and Audi Q6 e-tron. At the core of CARIAD is the development of the unified and scalable software and technology platform E³ 2.0 for all Group brands. E³ stands for ‘end to end electronics architecture’. The new software and technology platform is built from scratch and will be used first at the end of 2024 in Artemis, the lighthouse project of Audi. As of 2026, E³ 2.0 is due to be scaled by the Volkswagen brand with Trinity in the volume segment.
About our Group
CARIAD COE (Centre of Excellence) is setup in Bangalore, India working exclusively with CARIAD and associated VW groups with the focus on development of ‘VW.OS”. The areas of work involve development of platform components such as firmware, boot loaders, AUTOSAR, Linux/QNX BSP, Service oriented architecture-based middleware components.
Job Description:
We are looking for a Senior MLOps / ML Engineer to own, develop, and maintain ML deployment and data processing workflows, spanning an Argo-based workflow orchestration platform and Python-based frameworks for large-scale AI model inference.
The ideal candidate should have strong Python and problem-solving skills, be comfortable designing and troubleshooting workflows, and be able to quickly learn and adapt to new technologies. A pure MLOps background is not mandatory if the candidate has strong ML/software engineering fundamentals and can work effectively with technologies such as Databricks, Argo, Azure Pipelines, and Ray.
Job Description:
Infrastructure & Pipeline Development:
- Design and maintain Argo Workflows / Argo Events-based ML and data processing pipelines across environments (dev / QA / staging / prod).
- Design and maintain WorkflowTemplates, DAGs, fan-out patterns, scheduled workflows, and event-driven triggers following GitOps best practices.
- Design and develop Python-based workflows and components for ML data processing and model inference.
- Work with Databricks and Spark-based workloads for data processing, transformation, and pipeline execution.
- Build and maintain Docker images for ML and GPU-accelerated workloads, including multi-stage builds and container optimization.
- Develop scalable inference workflows using Ray where required.
- Integrate with Azure ML and Azure cloud services for model versioning, model artifacts, training workflows, and deployment.
- Maintain CI/CD pipelines with automated testing, quality gates, and environment-specific deployment processes.
Model Deployment & Operations:
- Automate deployment, monitoring, and rollback of machine learning and deep learning models on production Kubernetes environments.
- Manage model versioning and artifact movement from cloud ML workspaces and storage systems to production workloads.
- Maintain configuration systems and reusable workflow components to ensure reproducible pipeline execution across environments.
- Monitor model and pipeline performance, data processing health, resource utilization, and infrastructure reliability.
- Troubleshoot production issues involving failed workflows, data bottlenecks, resource contention, GPU utilization, and latency regressions.
- Identify reliability and performance issues proactively and implement improvements to ML and data processing workflows.
- Collaborate with data scientists and engineering teams to improve model deployment, inference, and pipeline reliability.
Expertise and Qualifications:
Must-Have Technical Skills
- Python - strong programming and problem-solving skills; experience with application development, scripting, and Python packaging.
- Workflow Design - ability to design reliable workflows with dependencies, parallel execution, retries, failure handling, scheduling, and event-driven execution.
- Argo Workflows / Argo Events - understanding of WorkflowTemplates, DAGs, event-driven workflows, and workflow execution.
- Databricks / Data Processing - hands-on experience with Databricks, Spark/Spark SQL, or similar large-scale data processing platforms.
- Kubernetes - understanding of workload orchestration, resource management, namespaces, and production troubleshooting.
- Git / Version Control - strong understanding of Git or equivalent version control systems, including branching, merging, pull requests, and maintaining code/configuration changes.
- Docker - containerization, multi-stage builds, and optimization of ML workloads.
- CI/CD - experience designing pipelines with automated testing, quality gates, artifact management, and deployment processes.
- Azure Cloud - working knowledge of Azure ML, Azure Blob Storage, AKS, and Azure-based ML workflows.
- Problem Solving - ability to troubleshoot unfamiliar technical problems and identify practical solutions.
Good-to-Have Technical Skills
- Ray - distributed data processing and inference at scale.
- Deep Learning frameworks - PyTorch, TensorFlow; model formats such as ONNX, SavedModel, and TorchScript.
- Helm / ArgoCD and GitOps-based deployment practices.
- Prometheus / Grafana and production observability.
- Experience with Azure Pipelines or similar CI/CD platforms.
- Experience with ML model monitoring, data drift, and model performance monitoring.
- Large-scale data processing - experience working with large volumes of structured, unstructured, image, or video data, particularly for ML/AI workloads.
Soft Skills
- Strong communication - explain technical concepts and infrastructure clearly to data scientists, engineers, and new team members.
- Ownership mindset - proactively identify and resolve reliability, performance, and workflow issues.
- Learning agility - quickly learn and adapt to new technologies, frameworks, and internal platforms.
- Collaboration - work effectively across data science, perception, DevOps, QA, and other engineering teams.
- Problem-solving under pressure - systematically triage and resolve production issues involving workflows, infrastructure, data, and model inference.
- Team player - contribute positively to the team, share knowledge, and work collaboratively toward common engineering goals.
Working with Embitel
- Freedom to challenge norms, make mistakes, learn and grow
- There is no ‘I’ in word ‘Team’ and no hierarchy at Embitel
- Employees of Embitel are partners of our excellence and growth. And they are at the core of organization’s Mission and Vision
To explore an exciting & progressive Career development at Embitel India:
- Visit us at: www.embitel.com
- Follow us at LinkedIn: https://www.linkedin.com/company/embitel-technologies-india