Client is looking for Real Time ML Engineer
- Deploy the ML models developed by data scientists in the production systems using GCP, BigQuery, and Airflow
- Collaborate with data scientists to seamlessly integrate model code (Python/PySpark) into the MLOps workflow.
- Resource and code Optimization of the ML model prediction scoring pipelines in GCP ecosystem so that the models run within the defined SLA
- Debug the ML model scoring failures for code and resource issues
- Orchestrate the model scoring pipelines using Cloud Composer
- Deploy Auto-ML solutions in the production systems
- Implement model performance monitoring (LIFT, ROC, Accuracy, and so), model stability monitoring, and feature/concept drift monitoring for all the new models
- Implement modernization practices such as observability and explainability for enhanced model monitoring and interpretability.
- Work closely with Responsible AI teams to ensure ethical AI principles and incorporate fairness, transparency, and accountability.
- Utilize Jenkins and GitLab for effective code management and version control.
- Leverage Vector DB(Milvus or any other), and LLM services in order to deploy LLM models
Qualifications:
- 8+ years of proven experience in AI/ML Engineering and minimum of 2 to 3 years of experience in GCP ecosystem is a must
- Master's or Bachelor's in Computer Science or in related fields; Concentration in Data Science is good to have
- Strong expertise in MLOPs best practices, CICD, and orchestration
- Basic to intermediate understanding on the working mechanism of each of the following: Classification (Random Forest, xgboost, catboost), Regression (Linear, Lasso, Ridge), Recommendation systems, dimensionality reduction techniques, deep learning (Neural Networks), and LLMs
- Proficient in PySpark and Python programming and GitLab for code management.
- Excellent communication skills for effective collaboration.
- Candidates holding Certification in Google Cloud and in Data Science are highly encouraged