Ml ops engineer
2 days ago
Role Brief:We are seeking a skilled ML Ops Engineer to design, implement, and maintain scalable machine learning and large language model (LLM) pipelines in cloud environments, primarily using AWS services. This role is critical to ensuring the reliability, efficiency, and performance of ML systems in production.The ideal candidate will have hands-on experience with AWS tools such as Sage Maker, Lambda, Bedrock, Batch with Fargate, and infrastructure components like RDS, Dynamo DB, and SQS. You will be responsible for automating CI/CD workflows, managing auto-scaling APIs, and provisioning cloud resources to support high-performance ML workloads, including RAG systems.Primary Responsibilities:- Strategizing and implementing scalable infrastructure for ML or LLM model pipelines using tools like and cloudservices such as AWS (e.g., AWS Batch, Fargate, Bedrock)- Manage auto-scaling mechanisms to handle varying workloads and ensure high availability of Rest APIs- Automate CI/CD pipelines and Lambda functions for model testing, deployment, and updates, reducing manual errorsand improving efficiency.- Amazon Sage Maker Pipelines for end-to-end ML workflow automation. Optimize utilizing step-functions- Conduct drift analysis to detect and respond to data drift, concept drift, and label drift. Implement mitigation strategies such as automated alerts, model retraining triggers, and performance audits.- Set up reproducible workflows for data preparation, model training, and deployment.- Provision and optimize cloud resources (e.g., GPUs, memory) to meet computational demands of large models like those used in RAG systems- Automate retraining workflows to keep models updated as data evolves- Work closely with data scientists, ML engineers, and Dev Ops teams to integrate models into production environments.- Implement monitoring tools to track model performance and detect issues like drift or degradation in real- time. Monitoring dashboards with real-time alerts for pipeline failures or performance issues C Implementing Model Observability frameworks.Required Skills:- Education Any Engineering (BE/Btech/ME/Mtech)- Min 4 years of experience with AWS services such as Lambda, Bedrock, Batch with Fargate, RDS (Postgre SQL), Dynamo DB, SQS, Cloud Watch, API Gateway, Sage Maker- Should have hands-on experience in drift analysis, including detecting and mitigating data, concept, and label drift in production ML systems- Knowledge of ML frameworks (e.g., Py Torch, Tensor Flow) to understand model requirements during deployment- Experience with Rest API Frameworks like Fast APIs, Flask- Familiarity with model observability like Evidently, Nanny ML, Phoenix and monitoring tools (Grafana etc) and retraining tools like MLflow/ Kubeflow / Airflow- AWS Certified Machine Learning – Specialty – Good to have this certification
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ML Ops Engineer
3 weeks ago
Bengaluru, India L&T Technology Services Full timeJob Title : ML Ops Engineer Location: Bengaluru Experience : 7+Years ML Ops Engineer Programming & Scripting, Data & Feature Engineering, Monitoring & Logging (Prometheus, Grafana), Experiment Tracking & Workflow Orchestration(MLflow, Kubeflow, Weights & Biases), knowledge of Machine Learning Frameworks (TensorFlow, PyTorch, Scikit-learn) Required...
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ML Ops Engineer
2 weeks ago
Bengaluru, India L&T Technology Services Full timeJob Title : ML Ops EngineerLocation: BengaluruExperience : 7+YearsML Ops EngineerProgramming & Scripting, Data & Feature Engineering, Monitoring & Logging (Prometheus, Grafana), Experiment Tracking & Workflow Orchestration(MLflow, Kubeflow, Weights & Biases), knowledge of Machine Learning Frameworks (TensorFlow, PyTorch, Scikit-learn)Required Skills: MLflow,...
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ML Ops Engineer
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ML Ops Engineer
7 days ago
Bengaluru, India L&T Technology Services Full timeJob Title : ML Ops Engineer Location: Bengaluru Experience : 7+Years ML Ops Engineer Programming & Scripting, Data & Feature Engineering, Monitoring & Logging (Prometheus, Grafana), Experiment Tracking & Workflow Orchestration(MLflow, Kubeflow, Weights & Biases), knowledge of Machine Learning Frameworks (TensorFlow, PyTorch, Scikit-learn) Required Skills:...
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ML Ops
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Bengaluru, Kochi, India 4seer Technologies Full time ₹ 8,00,000 - ₹ 12,00,000 per yearJob Description ML Ops Engineer (2–3 Years Experience)Position OverviewWe are looking for a passionate and skilled ML Ops Engineer with 2–3 years of experience to join our AI initiatives and services team. The ideal candidate will not only be strong in operationalizing machine learning workflows but also have hands-on exposure or working knowledge in...
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ML Ops Engineer
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Bengaluru, India Saarthee Full timeJob Summary: We are seeking a skilled ML Ops Engineer to support and enhance our machine learning operations infrastructure. In this role, you will be responsible for monitoring production services, troubleshooting issues, and collaborating with teams to improve automation and system reliability. You will play a critical role in ensuring seamless model...
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ML Ops Engineer
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Bengaluru, India Saarthee Full timeJob Summary: We are seeking a skilled ML Ops Engineer to support and enhance our machine learning operations infrastructure. In this role, you will be responsible for monitoring production services, troubleshooting issues, and collaborating with teams to improve automation and system reliability. You will play a critical role in ensuring seamless model...
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Ml ops engineer
2 days ago
Bengaluru, India Aurigo Software Technologies Full timeRole Brief:We are seeking a skilled ML Ops Engineer to design, implement, and maintain scalable machine learning and large language model (LLM) pipelines in cloud environments, primarily using AWS services. This role is critical to ensuring the reliability, efficiency, and performance of ML systems in production.The ideal candidate will have hands-on...
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LLM & ML Ops Engineer
2 weeks ago
Bengaluru, India Gainwell Technologies LLC Full timeJob Description Summary Gainwell is seeking LLM Ops Engineers and ML Ops Engineers to join our growing AI/ML team. This role is responsible for developing, deploying, and maintaining scalable infrastructure and pipelines for Machine Learning (ML) models and Large Language Models (LLMs). You will play a critical role in ensuring smooth model lifecycle...
