GCP Cloud ML/Ops Engineer
Job Summary
Job Description
This is a role for an experienced GCP Cloud ML/Ops Engineer to join a team building and deploying machine learning models at scale. You will be responsible for the entire ML lifecycle, from data processing and training to evaluation, deployment, serving, and monitoring. This is a hybrid role with 3 days a week in the office in Bangalore. You will have experience in public cloud services, particularly GCP, and be familiar with VertexAI / Kubeflow Pipelines, MLFlow, and other MLOps tools. You will have experience with machine learning frameworks and libraries, including TensorFlow, PyTorch, scikit-learn, and HuggingFace. You will also be knowledgeable about prompt engineering, LangChain, vector databases, and machine learning algorithms. You will have experience working with containerization technologies like Docker and Kubernetes, as well as Infrastructure as Code (IaC) tools like Terraform.
Must have:
- 4+ years experience in ML Ops
- 2+ years experience building end-to-end data pipelines
- 2+ years experience building and deploying ML models in GCP
- 3+ years experience working with SQL, Java, Python for data analysis/programming
- Technical degree: Computer Science, software engineering or related
Good to have:
- Experience with LLMOps concepts and practices
- Knowledge of prompt engineering methods
- Experience in Infrastructure and Applied DevOps principles
- Familiarity with model performance monitoring, data drift detection, and anomaly detection
- Strong understanding of IT infrastructure and operations
- Strong communication skills
- Willingness to adapt to new technologies
13 skills required
Job Details
Requirements:
• Expertise in public cloud services, particularly in GCP.
• Experience working with VertexAI / Kubeflow Pipelines or other MLOps tools like MLFlow.
• Experience with machine learning frameworks (TensorFlow, PyTorch) and libraries (e.g., scikit-learn, HuggingFace).
• Knowledge of prompt engineering methods, LangChain, Vector databases and machine learning algorithms.
• Knowledge of LLMOps concepts and practices, including model versioning, deployment, monitoring, and efficient resource utilization for large language models.
• Experience in Infrastructure and Applied DevOps principles utilizing tools for continuous integration and continuous deployment (CI/CD), and Infrastructure as Code (IaC) like Terraform to automate and improve development and release processes.
• Knowledge of containerization technologies such as Docker and Kubernetes to enhance the scalability and efficiency of applications.
• Proven experience in engineering machine learning systems at scale.
• Strong programming abilities in SQL, Java and Python.
• Proven experience with the entire ML lifecycle (processing, training, evaluation, deployment, serving, monitoring).
• Familiarity with model performance monitoring, data drift detection, and anomaly detection (hands-on experience preferred).
• Strong understanding of IT infrastructure and operations, with the ability to set up monitoring/alerting tools and access controls for both infrastructure and applications.
• Have strong communication skills to collaborate effectively with cross-functional teams.
• Demonstrate a willingness to adapt to new technologies and methodologies in the ever-evolving AIOps landscape.
Must Have:
• Overall 4+ years experience in ML Ops.
• 2+ years of experience building end-to-end data pipelines.
• 2+ years of experience building and deploying ML models in a GCP.
• 3+ years of experience working with SQL, Java, Python for data analysis.
programming.
• Technical degree: Computer Science, software engineering or related
• Based in Bangalore and willing to work from the office 3 days a week
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