Mlops Engineer / Lead @ IBM India

Job Description

Job Title: Mlops Engineer / Lead Job Type: Full-Time Location: Bangalore (Electronic City Only) Years of Experience: 8-12years Skill: MLops, Machine Learning,Deep Learning, ML Flow, Kube Flow & AirFlow, Docker, Bigdata Technologies, Hadoop,Hive,Spark,Pyspark, deploying ML Models JD: Machine Learning, Deep Learning Tensor flow,ML Flow, Kube Flow & Airflow. MLDE, Pachyderm" "Containerization (Docker) Experience in working with Docker containers, writing Dockerfile, building images, Docker image versioning & tagging, interacting with Docker engine to manage images & containers. Should have knowledge of installation & administration of container orchestration platforms like Kubernetes, RedHat OpenShift & Rancher. Good to have experience in Bigdata Technologies, Hadoop,Hive,Spark,Pyspark. Working experience in deploying ML Models." ============ If you are interested, please let me know the following details: # Your full name ( First : Middle : Last ) ( All expanded ): # Present. Employer Name & Work Location: # Payroll Company Name: # Highest Qualification (University Name and Passing year): # Relevant exp as a Mlops Engineer / Lead(in years): # Total IT experience: # Relevant exp in Deep Learning(in years): # Relevant exp in Machine Learning(in years): # Appian L2 (or above) certification (Yes/No): # Number of Appian BPM implementations: # Relevant exp in SQL (in years): # Current CTC: # Expected CTC: # Official Notice Period: # Are you serving notice period (if yes then mention Last working day): # Any offer you are holding (if yes please share the offer amount): # Current location: # Preferred location: # Reason for change (Please explain in detail): #DOB (Date Of Birth): ====== Thanks & Regards, Narayan Senior Team Leader Best Infosystems Ltd

Employement Category:

Employement Type: Full time
Industry: Recruitment Services
Role Category: General / Other Software
Functional Area: Not Applicable
Role/Responsibilies: Mlops Engineer / Lead

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Keyskills:   Machine Learning Deep Learning Docker Hadoop Hive Spark Containerization Kubernetes SQL MLops ML Flow Kube Flow AirFlow Bigdata Technologies Pyspark deploying ML Models Tensor flow MLDE Pachyderm RedHat OpenShift Rancher