• US Citizen

• Green Card

• EAD (OPT/CPT/GC/H4)

• H1B Work Permit

• Corp-Corp

• W2-Permanent

• W2-Contract

• Contract to Hire

• Consulting/Contract

• UG :- - Not Required

• PG :- - Not Required

• No of position :- ( 1 )

• Post :- 5th Dec 2023

• Design and implement machine learning solutions for customer use cases, leveraging core Google products including TensorFlow, DataFlow, and Vertex AI
• Work with customers to identify opportunities to apply machine learning in their business, deploy solutions and deliver workshops to educate and empower customers .
• Work closely with Product Management and Product Engineering to build and constantly drive excellence in our products.
• Support customer implementation of Google Cloud products through: architecture guidance,best practices, data migration, capacity planning, implementation, troubleshooting, monitoring, and much more. 

Responsibilities:

• Be a trusted technical advisor to customers and solve complex Machine Learning challenges.
• Create and deliver best practices recommendations, tutorials, blog articles, sample code, and technical presentations adapting to different levels of key business and technical stakeholders.
• Work with Customers, Partners, and Google Product teams to deliver tailored solutions into production.
• Coach customers on the practical challenges in ML systems: feature extraction/feature definition, data validation, monitoring, and management of features/models. 

Minimum Qualifications:

• Bachelor degree in Computer Science, Mathematics or a related technical field or equivalent practical experience.
• Hands-on experience building machine learning solutions.
• Experience coding in one or more languages such as Python, Scala, Java, Go, or similar with strong competencies in data structures, algorithms, and software design.
• Experience working with technical customers.

Preferred Qualifications:

• A solid understanding of the auxiliary practical concerns in production ML systems
• Experience working with recommendation engines, data pipelines, or distributed machine learning
• Experience with deep learning frameworks (such as Tensorflow, pyTorch, XGBoost).
• Knowledge of data warehousing concepts, including data warehouse technical architectures,infrastructure components, ETL/ ELT and reporting/analytic tools and environments (such as Apache Beam, Hadoop, Spark, Pig, Hive, MapReduce, Flume).
• Relevant experience in technical consulting.