Accenture | Jobs | ACN – Applied Intelligence – GCP – Data Engineering – 08 | BigDataKB.com | 27-03-22

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Job Location: Pune

Experience & qualifications
โ€ขPractical experience in the field of Machine Learning with experience developing and architecting software, conversant with full lifecycle from prototype to production.
โ€ขTechnical know-how of AI/ML scenarios and operational challenges in production.
โ€ขApplying engineering principles to develop and deploy ML models in medium to large scale environment.
โ€ขProven experience with machine learning offerings in the Google Cloud Platform. Cloud certifications (around Azure/AWS/GCP is a plus).
โ€ขExperience managing key elements of a data and ML platform: scalable data pipelines, feature stores, data lifecycle, model store, model deployment and monitoring, ML pipelines.
Career Level Years Of Exp
L7 10-14 years
L8 7-10 years
L9 4-7 years
L10 2-4 years

โ€ขExperience of deploying models in a production environment (knowledge of modern pipeline frameworks like Kubeflow/TensorFlow Extended (TFX)
โ€ขA leader in exceptional software engineering practices including coding standards, reviews, testing and operations.
โ€ขStrong experience in agile practices and CI/CD
โ€ขHands-on experience in development, deployment and operation of data technologies and platforms such as:
1)Integration โ€“ APIs, micro-services and ETL/ELT patterns
2)DevOps โ€“ Ansible, Jenkins, ELK
3)Version Control โ€“ Git, Bitbucket, native tools etc.
4)Containerization โ€“ Docker, Kubernetes etc.
5)Orchestration โ€“ Airflow, Cloud Composer, Kubeflow etc.
6)Languages and scripting: Python, Scala Java etc
7)Cloud Services โ€“ Google Cloud Platform and native tools
8)Analytics and ML tooling โ€“ Vertex AI, Sagemaker, ML Studio
9)Execution Paradigm โ€“ low latency/Streaming, batch and micro batch processing
10)Data platforms โ€“ Big Data (Dataproc, Hadoop, Spark, Hive, Kafka etc.) and Data Warehouse (BigQuery, Teradata, Redshift, Snowflake etc.)
11)Visualization Tools โ€“ Looker, PowerBI, Tableau

โ€ขWillingness and ability to learn quickly and apply creative thinking to finding great solutions and drive them to completion.
โ€ขExperience working in a multi-disciplinary team where you enjoyed being the technical expert and enabling others.
โ€ขDemonstrated ability to work with cross-functional IT/Data Science teams in a highly innovative and fast-paced environment.
โ€ขExcellent verbal, written, and effective communication skills in English.

Additional (Good to have) Skills
โ€ขFamiliarity with contemporary Google Cloud Architectures, Virtualization and Containerization methods, tools, and techniques on GCP.
โ€ขFamiliarity or knowledge on resource utilization and provisioning viz. TPU, GPU and GKE.
โ€ขBilling estimates of training and prediction on Google Cloud Platform.
โ€ขMemory profiling of Model components using open source libraries.
โ€ขVarious hosting techniques for Online prediction besides Flask API(s).
โ€ขFamiliarity with model training and deployment using AutoML on GCP.
โ€ขFamiliarity with Google tools โ€“ AutoML, Conversational AI, AI for industries, AI for documents is a plus.
โ€ขFamiliarity with Cloud security, networking topics is a plus.
โ€ขFamiliarity with modern ML platforms like H20, Databricks, Dataiku is a plus
Job description
Do you want to develop intelligent solutions for our customers and successfully implement machine learning models in real environments? Models that can transition seamlessly across environments with ability to (re)train & deploy near real time?
As a Google Cloud Platform (GCP) ML Engineer you are an expert engineer with an eye for AI. You will be required to develop a holistic understanding of the AI/ML solution you are building, including transferring some of the software engineering best practices to the data science world.
Having a deep understanding of the mathematical underpinnings of the Machine Learning algorithms is a must have, as you will be required to know what algorithms are available and when and how to apply them.

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