Job description

Job Summary

We are seeking a highly experienced Senior Data Scientist, Machine Learning Engineer with strong exposure in DevOps (ML Platform/ GCP) with 8+ years of experience to drive AI/ML, infrastructure automation, CI/CD, containerization, and ML platform operations on GCP

initiatives for the RAPTOR platform. The role focuses on ML platform engineering, Vertex AI-based solutions, real-time inference, and scalable ML pipelines while supporting platform modernization and AI-driven improvements.
The candidate will work across incident response, request processing, ML platform support, and developer enablement, with a strong focus on Google Cloud Platform (GCP) and Vertex AI ecosystem.

Key Responsibilities

Design, build, and maintain scalable ML pipelines and orchestration frameworks using Vertex AI and Kubeflow.
Develop and operationalize machine learning models for batch and real-time inference.
Manage end-to-end ML lifecycle: data ingestion, training, evaluation, deployment, monitoring, and retraining.
Collaborate on platform modernization initiatives and introduce automation across RAPTOR services.
Support incident response, root cause analysis, and production troubleshooting for ML systems.
Implement monitoring and alerting frameworks for model performance and failures.
Develop and manage REST APIs for model integration and platform services.
Work on real-time streaming and event-driven ML architectures using Pub/Sub and Dataflow.
Provide developer support for RAPTOR platform and GCP tooling.
Contribute to self-service AI capabilities and automation/monitoring frameworks.
Support ML/AI platform operations (Vertex AI, Gemini, inference systems).
Build and maintain CI/CD pipelines for ML and platform services
Automate infrastructure provisioning using Terraform and Ansible
Manage containerized workloads using Docker and Kubernetes
Ensure high availability, scalability, and system reliability
Support incident response, monitoring, logging, and alerting systems
Optimize deployment pipelines for ML models and data workflows
Manage IAM roles, service accounts, and access control policies
Enable self-service operations and automation frameworks

Provide platform support to ML engineers and data teams

Skill Requirements

1. Core ML & Data Science Skill Category ML model development, evaluation, deployment Must Have ML pipelines & orchestration (Vertex AI Pipelines / Kubeflow) Must Have Real-time inference & streaming ML systems Must Have Model monitoring (drift, failure detection, alerting) Must Have Feature engineering & model optimization Good to Have 2. GCP & AI Platform Vertex AI & ML Platform Skill Category Vertex AI (Batch training, inference, pipelines) Must Have Vertex AI Model Monitoring Must Have AutoML Must Have Gemini / LLM capabilities Good to Have 3. Data & Processing Skill Category BigQuery (SQL, large-scale data pr

Other Requirements

1.Relevant certifications in Google BigQuery, SQL, Python, Google dataflow, or CA Erwin Data Modeler are a plus.

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