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Machine Learning Operations Specialist - VOIS at Vodafone United States
Pune, maharashtra, India -
Full Time

Start Date

Immediate

Expiry Date

02 Jan, 26

Salary

0.0

Posted On

04 Oct, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

No

Skills

Machine Learning, Cloud Platforms, GCP, AI Platform, Dataflow, BigQuery, Deep Learning, NLP, Kubernetes, TensorFlow, CI/CD, Version Control, Unit Testing, Integration Testing, Containerization, Orchestration

Industry

Telecommunications

Description
Automate and optimise machine learning workloads across cloud platforms Deploy, monitor, and scale ML models in production environments Validate key performance indicators such as latency, throughput, model accuracy, and scalability metrics Collaborate with cross-functional teams to ensure seamless integration of ML solutions Utilise GCP tools including AI Platform, Composer, Scheduler, Container Registry, Dataflow, BigQuery, Datastore, and Compute Engine Apply advanced analytics and modelling techniques including Random Forest, SMA, ARIMA, Prophet, Deep Learning (LSTM, CNN, RNN), and NLP Work with containerisation and orchestration tools such as Kubernetes, TensorFlow, and Kubeflow Performance monitoring and KPI validation for ML models A professional with 4-6 years of experience in ML Operations and AI Proficient in Google Cloud Platform and familiar with Azure Skilled in CI/CD, version control, unit and integration testing Experienced in deploying and managing ML workloads Knowledgeable in data science methodologies and deep learning frameworks Comfortable working with containerisation and orchestration technologies If you are excited about this role but your experience does not align exactly with every aspect of the job description, you are encouraged to apply. You may be the right candidate for this or another opportunity, and the recruitment team will support you in exploring where your skills fit best.

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Responsibilities
Automate and optimize machine learning workloads across cloud platforms while deploying, monitoring, and scaling ML models in production environments. Collaborate with cross-functional teams to ensure seamless integration of ML solutions and validate key performance indicators.