Sr. Data Engineer,Location : 100./. REMOTE ,Duration: 6+ Months contract

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Role: Sr. Data Engineer

Location : 100./. REMOTE

Experience: 9+ Years

Duration: 6+ Months contract

Day to Day Responsibilities:

• Lead the integration of machine learning models into business-critical applications using
• Google Cloud Platform Vertex AI.
• Collaborate with data engineers, data scientists, software engineers, and product owners to
• ensure seamless model deployment and performance in production environments.
• Design and implement scalable, resilient, and secure model inference pipelines using Vertex
• AI, Vertex Pipelines, and related services.
• Enable continuous delivery and monitoring of models via Vertex AI Model Registry, Prediction
• Endpoints, and Model Monitoring features.
• Optimize model serving performance, cost, and throughput under high-load, real-time, and
• batch scenarios.
• Automate model lifecycle management including CI/CD pipelines, retraining, versioning,
• rollback, and shadow testing.
• Participate in architecture reviews and advocate best practices in ML model orchestration,
• resource tuning, and observability.

Required Skills:

• 2 years - Strong experience in model integration and deployment using Google Cloud Platform Vertex AI, especially
• around Vertex Pipelines, Endpoints, Model Monitoring, and Feature Store.
• Expertise in scaling ML models in production, including load balancing, latency optimization,
• A/B testing, and automated retraining pipelines.
• Proficiency in MLOps and model operationalization techniques, with knowledge of
• infrastructure-as-code and containerized environments.

Software Skills

Required:

• Google Cloud Platform Vertex AI Suite (including Pipelines, Feature Store, Model Monitoring)
• Python (with emphasis on integration frameworks and automation)
• Git, Docker, Poetry, Terraform or Deployment Manager
• BigQuery, Dataflow, and Cloud Functions
• Monitoring tools (Stackdriver, Prometheus, etc.)

Preferred Skills:

• Experience with MLOps tools such as Kubeflow, MLFlow, or TFX.
• Familiarity with enterprise monitoring tools Prometheus, Grafana, or Stackdriver for ML observability.
• Exposure to hybrid or federated model deployment architectures.

Disqualifiers:

• Resumes more than 3-4 pages in length.
• Generic resumes without clearly defined accomplishments or project impact.

Missing valid LinkedIn profile.