Accenture | Hiring | ACN – Applied Intelligence – GCP – Machine Learning Engineer – 09 | BigDataKB.com | 07-04-2022

Job Location: Bangalore/Bengaluru

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.

Roles & Responsibilities
โ€ข 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.
โ€ข Be an Agile learner with ability to adapt to new sets of emerging tools & technologies including proprietary Accenture solutions & methodologies.
โ€ข 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.
โ€ข Design, develop, test, and deploy data pipelines, machine learning infrastructure and client-facing products and services. Scale existing ML models into production
โ€ข Know how to address technical problem solutions and implement them in practice with the help of native tools on Google Cloud Platform
โ€ข Ability to make decisions and take responsibility for projects and tasks
โ€ข Design, implement, test, and productionize the models on Google Cloud Platform using native components.
โ€ข Analyze and resolve architectural problems, working closely with engineering, data science and operations teams
โ€ข Perform technical architecture assessments and provide improvements and focus areas
โ€ข Provide best-practice knowledge, reference architectures, and patterns for use across ML engineering and architecture communities
โ€ข Communicate and provide guidance to senior client leadership and teams
โ€ข Contribute ML Engineering expertise to team and new sales activities

Experience & qualifications
โ€ข Practical experience in the field of Machine Learning with experience developing and architecting software, conversant with full lifecycle from prototype to production.
โ€ข Technical know-how of AI/ML scenarios and operational challenges in production.
โ€ข Applying engineering principles to develop and deploy ML models in medium to large scale environment.
โ€ข Proven experience with machine learning offerings in the Google Cloud Platform. Cloud certifications (around Azure/AWS/GCP is a plus).
โ€ข 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.
โ€ข Experience of deploying models in a production environment (knowledge of modern pipeline frameworks like Kubeflow/TensorFlow Extended (TFX)
โ€ข Strong experience in agile practices and CI/CD
โ€ข 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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