Senior Big Data Architect AI

Role Description:

In this role you will be part of a leading team of data scientists and machine learning engineers working on SafeRide’s award winning AI product in the automotive cybersecurity space.

You will be responsible for architecting, implementing, optimizing and deploying machine learning models at huge scales working with a wide range of deployment solutions – both on-prem and cloud based.

A large part of these responsibilities will be to help evolve the capacity and reliability of the platform engine into a more agile and nimble architecture and expand into both on-prem and cloud-native offerings leveraging virtualization technologies using a software engineering and DevOps orientation.

Responsibilities:

  • Create and maintain optimal data and model DataOps pipeline architecture
  • Design and create solutions to improve scalability and performance of machine learning models
  • Facilitate use cases for Kubernetes workload through development of container (Docker) services
  • Become the data guru for the product management, data science and engineering groups to facilitate the design and implementation of scalable AI models
  • Periodically evaluate and optimize performance and platform throughput
  • Build the infrastructure required for optimal extraction, transformation, and loading of data from a wide variety of data sources and executing a scalable AI engine on this data

Minimum Requirements

  • Sc. and above in CS/EE or equivalent
  • Strong understanding of CS fundamentals – computer architecture, performance complexity, data structures and algorithms
  • Experience with development of analytics and data science-based solutions using open source tools and libraries is a significant advantage
  • At least 5 years of hands-on experience with AI tools, frameworks and languages – Spark, Hadoop, Kafka, ElasticSearch, Scala, Python, GO, R, SQL, SkLearn, NLTK, NumPy, Pandas, TensorFlow, Keras
  • At least 3 years of hands-on experience with Dockers and Kubernetes
  • Working knowledge of message queuing, stream processing, and highly scalable ‘big data’ data stores
  • Experience supporting and working with cross-functional teams in a dynamic environment

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