Job Location: Bangalore/Bengaluru
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 & Responsibilities
โข 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.
โข Be an Agile learner with ability to adapt to new sets of emerging tools & technologies including proprietary Accenture solutions & methodologies.
โข 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.
โข Design, develop, test, and deploy data pipelines, machine learning infrastructure and client-facing products and services. Scale existing ML models into production
โข Know how to address technical problem solutions and implement them in practice with the help of native tools on Google Cloud Platform
โข Ability to make decisions and take responsibility for projects and tasks
โข Design, implement, test, and productionize the models on Google Cloud Platform using native components.
โข Analyze and resolve architectural problems, working closely with engineering, data science and operations teams
โข Perform technical architecture assessments and provide improvements and focus areas
โข Provide best-practice knowledge, reference architectures, and patterns for use across ML engineering and architecture communities
โข Communicate and provide guidance to senior client leadership and teams
โข Contribute ML Engineering expertise to team and new sales activities
Experience & qualifications
โข Practical experience in the field of Machine Learning with experience developing and architecting software, conversant with full lifecycle from prototype to production.
โข Technical know-how of AI/ML scenarios and operational challenges in production.
โข Applying engineering principles to develop and deploy ML models in medium to large scale environment.
โข Proven experience with machine learning offerings in the Google Cloud Platform. Cloud certifications (around Azure/AWS/GCP is a plus).
โข 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.
โข Experience of deploying models in a production environment (knowledge of modern pipeline frameworks like Kubeflow/TensorFlow Extended (TFX)
โข Strong experience in agile practices and CI/CD
โข Hands-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
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