Bosch Group: DevOps/MLOps Specialist — bengaluru, India

About Bosch Group

Bosch's India activities span mobility, industrial technology, consumer goods, and energy and building technology. Its employer description identifies engineering development, manufacturing and commercial operations within that range. The group operates through multiple Indian businesses, including activities in automotive systems, software and engineering, industrial technology and home appliances. This makes Bosch a collection of related technology businesses rather than a single product operation, with different entities serving different industrial and consumer applications.

The position and its setting

Bosch Group lists DevOps/MLOps Specialist in bengaluru, India, under reference REF291510A. The details below separate the work described by the employer from essential conditions, preferred experience and practical preparation advice.

• Employment type: Full-time
• Employer listing date: 12 October 2026
• Contract type: Unlimited
• Legal Entity: Bosch Global Software Technologies Private Limited
• Brands: Bosch Group

Subjects named in the job description

The posting mentions the following work areas. These references are not a separate list of compulsory certifications.

• Kafka
• Kubernetes
• CI/CD
• Docker
• AWS
• GCP
• Azure
• AI/ML
• LLMs
• Google Cloud

Essential requirements and candidate background

• Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related field.5+ years of experience in MLOps with at least 2 years working specifically with LLMOps pipelines for large language models
• Experience: deploying, managing, and optimizing machine learning models in cloud environments at scale
• Problem-solving abilities and a passion for building scalable, production-grade AI systems
• Lead the design and implementation of end-to-end MLOps and LLMOps pipelines, from model development and training to deployment and continuous monitoring in production environments
• Develop automation for model deployment and management, ensuring scalability, efficiency, and cost-effectiveness, particularly for large language models (LLMs) in cloud environments
• Implement and manage CI/CD pipelines for both ML and LLM models, integrating them seamlessly into the production environment
• Collaborate with data scientists, ML engineers, and software developers to ensure the integration of LLMs and ML models into scalable, production-ready systems
• Build data pipelines and workflows for training and serving models, ensuring efficient data processing and pipeline orchestration using tools like Kubeflow, Airflow, or MLflow
• Create robust monitoring systems for tracking model performance, diagnosing issues, and ensuring the reliability and health of both traditional ML and LLM-based systems
• Optimize the performance of deployed models, focusing on cost optimization, speed, and accuracy, including hardware acceleration (GPUs, TPUs)
• Maintain version control, governance, and model lifecycle management for both ML and LLM models using industry-standard tools
• Stay up-to-date with the latest advancements in MLOps, LLMOps, and Generative AI, implementing best practices and innovative approaches to improve the deployment lifecycle
• MLOps & LLMOps Expertise: understanding of MLOps best practices and LLMOps workflows for large language models, ensuring seamless integration of AI systems into production environments
• Cloud Infrastructure & Deployment: Extensive experience with cloud platforms (AWS, GCP, Azure) for deploying and scaling ML/LLM solutions
• Expertise in containerization (Docker, Kubernetes) for model deployment and orchestration
• CI/CD Pipelines & Automation: Proficiency in developing and managing CI/CD pipelines for machine learning models using tools like Jenkins, GitLab CI, or CircleCI, and automation tools like Terraform or Ansible
• Model Serving & Monitoring: Hands-on experience deploying models using frameworks such as TensorFlow Serving, TorchServe, Kubeflow, or MLflow
• Understanding: model monitoring and alerting (e.g., Prometheus, Grafana)
• Machine Learning Frameworks: knowledge of popular machine learning frameworks like TensorFlow, PyTorch, Hugging Face, and others, with a particular focus on deploying and fine-tuning large language models (e.g., GPT, BERT)
• Data Engineering & Pipelines: Expertise in building and managing robust data pipelines using tools like Apache Kafka, Apache Airflow, or Apache Beam to support ML and LLM workflows

Education and qualification routes

• Degree / qualification: BE, MCA, MTech, BTech

Preferred experience and additional strengths

• Experience: AI and Generative AI technologies is a plus

Location, schedule and contract conditions

• Work arrangement: On-site
• Advertised location: bengaluru, India

Pay and application dates

A verified salary figure is not established in this article. An online estimate for another DevOps/MLOps Specialist vacancy is not an offer for reference REF291510A; use the employer’s own pay disclosure and recruitment communication.

A closing date is not confirmed in this article. Check the current employer page before submitting: an undated posting can change or be withdrawn.

How to apply to Bosch Group

Applications follow the official employer page linked below.

Open the official Bosch Group posting below and match the title and reference REF291510A.

Compare your qualifications and experience with the essential conditions for the bengaluru, India position, then read the preferred criteria separately.

Prepare an accurate CV and the supporting items requested in that employer’s application. Provide only information and documents you are authorised to share.

Use the employer’s application option, complete the required fields and review the submission. Retain the acknowledgement when one is provided. Job Alerts World does not receive the application.

Practical preparation: Kubernetes

Kubernetes references can cover deploying workloads, managing configuration or operating a cluster. Explain which of those you did, how the application was packaged and how you investigated failures or verified a release.

Relevant skills

• Kafka
• Kubernetes
• CI/CD
• Docker
• AWS
• GCP
• Azure
• AI/ML
• LLMs
• Google Cloud
• Terraform
• machine learning