Capgemini LLMOps Engineer Job in Bangalore: Apply Now
Capgemini
View All Jobs from Capgemini →Capgemini is hiring for an LLMOps Engineer Job to design, implement, and maintain end-to-end machine learning pipelines in Bangalore. In this hybrid role, you will collaborate with data scientists and software engineers to operationalize ML models and manage GenAI scalability. The position focuses on utilizing serving frameworks like TensorFlow Serving and TorchServe, alongside robust CI/CD and MLOps tools, to optimize infrastructure for performance, cost-efficiency, and seamless enterprise integration.
Quick Job Details
What You'll Do
As an LLMOps Engineer, you will manage the end-to-end deployment, monitoring, and optimization of machine learning and GenAI solutions.
-
Design, implement, and maintain end-to-end ML pipelines for model training, evaluation, and deployment.
-
Collaborate with data scientists and software engineers to operationalize ML models using serving frameworks and MLOps tools.
-
Develop and maintain CI/CD pipelines specifically for ML workflows.
-
Implement robust monitoring and logging solutions for ML models.
-
Optimize ML infrastructure for performance, scalability, and cost-efficiency based on LLMOps monitoring data.
-
Provide Ops support for Large Language Models, handling incident management and user support.
-
Monitor and maintain GenAI solutions, tracking performance, user interactions, and overall efficacy.
-
Conduct guardrails review and monitoring, including bias audits and identified guardrail checks for implemented GenAI solutions.
-
Provide code management support, focusing on proactive code optimization beyond standard break fixes.
Get Latest 📰 Job Updates
Never miss an opportunity! Join our official channels for daily job alerts and career tips.
What You Need (Requirements)
To succeed in this role, you must have strong hands-on experience in machine learning operations, model serving, and generative AI integrations.
-
Experience in designing and maintaining ML pipelines and CI/CD workflows for machine learning.
-
Hands-on experience with ML model serving frameworks such as TensorFlow Serving and TorchServe.
-
Strong proficiency in utilizing modern MLOps tools to operationalize models.
-
Proven experience managing GenAI scalability, integration, and infrastructure optimizations.
-
Ability to implement and monitor AI guardrails, including conducting bias audits on GenAI solutions.
-
Strong background in proactive code management and optimization.
-
Excellent problem-solving skills for handling incident management, root cause analysis, and ops support.
Tech Stack
💡 Gigsdock Pro-Tip
To stand out for this LLMOps Engineer Job, emphasize your hands-on experience operationalizing large language models and integrating GenAI solutions at scale. During your technical interview, be prepared to discuss the specific MLOps tools and serving frameworks you have utilized, such as TensorFlow Serving or TorchServe, to optimize infrastructure cost and performance. Highlight your understanding of AI governance—specifically how you implement guardrails and monitor for biases—as this is a critical component of enterprise GenAI deployments at companies like Capgemini.
⚠️ Disclaimer & Important Information
- Never pay any amount for getting a job. Genuine employers never charge recruitment fees.
- Apply before the hiring closes or the employer stops accepting applications.