Delhi, Delhi
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, infe rence 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
Skills & Qualifications (Categorized)
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
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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
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3. Data & Processing
Skill Category
BigQuery (SQL, large-scale data processing) Must Have
Dataflow (Apache Beam – batch & streaming) (Java SDK) Must Have
Cloud Composer (Apache Airflow) Must Have
Google Pub/Sub Must Have
Cloud Storage (Buckets) Good to Have
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4. Compute & Runtime
Skill Category
Cloud Run / Cloud Functions Must Have
App Engine (Flex & Standard) Must Have
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5. Integration & Engineering
Skill Category
REST APIs development & integration Must Have
Microservices / service-based architecture understanding Must Have
Observability (logging, monitoring, tracing) Must Have
Prompt engineering & evaluation Good to Have
Google Chat API integration Bonus
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6. Programming & Tools
Skill Category
Python (primary language, automation scripts) Must Have
SQL (strong working knowledge) Must Have
Bash scripting Must Have
GCP Python client libraries Must Have
gcloud SDK Must Have
Java (Apache Beam / Dataflow use cases) Good to Have
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