5+ Years

The candidate will be required on Deqodes payroll.

Person who understands end 2 end model lifecycle
Prod exposure
Experience in building alerting systems
Understands how LLMs observability works
Experience with production deployment and troubleshooting production issues
Knows how to tackle, track issues and debug
Azure platform experience is a must, along with experience in one tool for monitoring and observability outside Azure

Bachelors degree in Computer Science, Engineering or Technical Field preferred.
Minimum 3-5 years of relevant experience.
Proven experience in machine learning engineering and operations.
Profound understanding of machine learning concepts, model lifecycle management, and experience in model management capabilities including model definitions, performance management and integration.
Execution of model deployment, monitoring, profiling, governance and analysis initiatives.
Excellent interpersonal, oral, and written communication; Ability to relate ideas and concepts to others; write reports, business correspondence, project plans and procedure documents.
Solid Python, ML frameworks (e.g.,TensorFlow,



PyTorch), data modeling, and programming skills.
Experience and strong understanding of cloud architecture and design (AWS, Azure, GCP).
Experience using up-to-date approaches to automating machine learning pipelines.
Agile and Waterfall methodologies.
Ability to work independently and manage multiple task assignments within a structured implementation methodology.
Personally invested in continuous improvement and innovation.
Motivated, self-directed individual that works well with minimal supervision.
Must have experience working across multiple teams/technologies.
Preferred but not essential: Experience with business intelligence tools (preferably PowerBI).
Preferred but not essential: Experience with MLOps tools (e.g., MLflow, Kubeflow).

Disclaimer : This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

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