Sr. Delivery Consultant-ai/ml, Proserve

Amazon Amazon · Big Tech · IN, KA, Bengaluru · Data Science

This role involves guiding enterprise customers in building and deploying ML/DL models on AWS, from understanding business needs to operationalizing models and addressing model drift. It requires experience with deep learning frameworks and ML platforms.

What you'd actually do

  1. Understand the internal customer’s business need and guide them to a solution using our AWS AI Services, AWS AI Platforms, AWS AI Frameworks, and AWS AI EC2 Instances .
  2. Assist internal customers by being able to deliver a ML / DL project from beginning to end, including understanding the business need, aggregating data, exploring data, building & validating predictive models, and deploying completed models to deliver business impact to the organization.
  3. Use Deep Learning frameworks like MXNet, Caffe 2, Tensorflow, Theano, CNTK, and Keras to help our internal customers build DL models.
  4. Use SparkML and Amazon Machine Learning (AML) to help our internal customers build ML models.
  5. Work with our Professional Services Big Data consultants to analyze, extract, normalize, and label relevant data.

Skills

Required

  • 7+ years of external or internal customer facing, complex and large scale project management experience
  • Bachelor's degree in computer science, engineering, mathematics or equivalent
  • Experience driving collaborative projects from conception to delivery, or experience with training and deploying machine learning systems to solve large-scale optimizations
  • Experience with popular deep learning frameworks such as MxNet and Tensor Flow
  • 9+ years of building machine learning models for business application experience
  • Experience with AI/ML technologies

Nice to have

  • 10+ years of IT platform implementation in a technical and analytical role experience
  • Experience in consulting, design and implementation of serverless distributed solutions
  • Experience as technical specialist in design and architecture
  • Experience in cloud based solution (AWS or equivalent), system, network and operating system
  • Experience in database (eg. SQL, NoSQL, Hadoop, Spark, Kafka, Kinesis)
  • Experience in external or internal customer facing, complex and large scale project management

What the JD emphasized

  • building & validating predictive models
  • deploying completed models
  • building ML models
  • building DL models
  • operationalize models
  • retraining models
  • implement novel ML and DL approaches

Other signals

  • customer-facing
  • consulting
  • ML/DL model building
  • deployment
  • AWS AI Services