Job Description

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Experience andamp; qualificationsandlt;br or andgt; • Practical experience in the field of Machine Learning with experience developing and architecting software conversant with full lifecycle from prototype to production.andlt;br or andgt; • Technical know-how of AI or ML scenarios and operational challenges in production.andlt;br or andgt; • Applying engineering principles to develop and deploy ML models in medium to large scale environment.andlt;br or andgt; • Proven experience with machine learning offerings in the Google Cloud Platform. Cloud certifications .around Azure or AWS or GCP is a plus.. andlt;br or andgt; • Experience managing key elements of a data and ML platform: scalable data pipelines feature stores data lifecycle model store model deployment and monitoring ML pipelines.andlt;br or andgt; Career Level Years Of Expandlt;br or andgt; L7 10-14 yearsandlt;br or andgt; L8 7-10 yearsandlt;br or andgt; L9 4-7 yearsandlt;br or andgt; L10 2-4 yearsandlt;br or andgt; andlt;br or andgt; • Experience of deploying models in a production environment .knowledge of modern pipeline frameworks like Kubeflow or TensorFlow Extended .TFX.andlt;br or andgt; • A leader in exceptional software engineering practices including coding standards reviews testing and operations.andlt;br or andgt; • Strong experience in agile practices and CI or CDandlt;br or andgt; • Hands-on experience in development deployment and operation of data technologies and platforms such as:andlt;br or andgt; 1. Integration – APIs micro-services and ETL or ELT patterns andlt;br or andgt; 2. DevOps – Ansible Jenkins ELK andlt;br or andgt; 3. Version Control – Git Bitbucket native tools etc. andlt;br or andgt; 4. Containerization – Docker Kubernetes etc.andlt;br or andgt; 5. Orchestration – Airflow Cloud Composer Kubeflow etc.andlt;br or andgt; 6. Languages and scripting: Python Scala Java etcandlt;br or andgt; 7. Cloud Services – Google Cloud Platform and native toolsandlt;br or andgt; 8. Analytics and ML tooling – Vertex AI Sagemaker ML Studioandlt;br or andgt; 9. Execution Paradigm – low latency or Streaming batch and micro batch processing andlt;br or andgt; 10. Data platforms – Big Data .Dataproc Hadoop Spark Hive Kafka etc.. and Data Warehouse .BigQuery Teradata Redshift Snowflake etc.. andlt;br or andgt; 11. Visualization Tools – Looker PowerBI Tableauandlt;br or andgt; andlt;br or andgt; • Willingness and ability to learn quickly and apply creative thinking to finding great solutions and drive them to completion.andlt;br or andgt; • Experience working in a multi-disciplinary team where you enjoyed being the technical expert and enabling others.andlt;br or andgt; • Demonstrated ability to work with cross-functional IT or Data Science teams in a highly innovative and fast-paced environment.andlt;br or andgt; • Excellent verbal written and effective communication skills in English.andlt;br or andgt;
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Additional .Good to have. Skills• Familiarity with contemporary Google Cloud Architectures Virtualization and Containerization methods tools and techniques on GCP.• Familiarity or knowledge on resource utilization and provisioning viz. TPU GPU and GKE.• Billing estimates of training and prediction on Google Cloud Platform.• Memory profiling of Model components using open source libraries.• Various hosting techniques for Online prediction besides Flask API.s..• Familiarity with model training and deployment using AutoML on GCP. • Familiarity with Google tools – AutoML Conversational AI AI for industries AI for documents is a plus.• Familiarity with Cloud security networking topics is a plus. • Familiarity with modern ML platforms like H20 Databricks Dataiku is a plusJob descriptionDo 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 andamp; 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 or 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. Roles andamp; Responsibilities• Drive the vision for modern data and analytics platform to deliver well architected and engineered data and analytics products leveraging Google cloud tech stack and third-party products.• Be an Agile learner with ability to adapt to new sets of emerging tools andamp; technologies including proprietary Accenture solutions andamp; methodologies.• As part of global team working in collaboration with Data Engineers and Data Scientists ensure that ML models and pipelines are successfully implemented in real productive environments.• Design develop test and deploy data pipelines machine learning infrastructure and client-facing products and services. Scale existing ML models into production• Know how to address technical problem solutions and implement them in practice with the help of native tools on Google Cloud Platform• Ability to make decisions and take responsibility for projects and tasks• Design implement test and productionize the models on Google Cloud Platform using native components.• Analyze and resolve architectural problems working closely with engineering data science and operations teams• Perform technical architecture assessments and provide improvements and focus areas• Provide best-practice knowledge reference architectures and patterns for use across ML engineering and architecture communities • Communicate and provide guidance to senior client leadership and teams• Contribute ML Engineering expertise to team and new sales activities

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