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
• 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 & technologies including proprietary Accenture solutions & 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
Experience & qualifications
• Practical experience in the field of Machine Learning with experience developing and architecting software, conversant with full lifecycle from prototype to production.
• Technical know-how of AI/ML scenarios and operational challenges in production.
• Applying engineering principles to develop and deploy ML models in medium to large scale environment.
• Proven experience with machine learning offerings in the Google Cloud Platform. Cloud certifications (around Azure/AWS/GCP is a plus).
• 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.
• Experience of deploying models in a production environment (knowledge of modern pipeline frameworks like Kubeflow/TensorFlow Extended (TFX)
• Strong experience in agile practices and CI/CD
• Hands-on experience in development, deployment and operation of data technologies and platforms such as:
1) Integration – APIs, micro-services and ETL/ELT patterns
2) DevOps – Ansible, Jenkins, ELK
3) Version Control – Git, Bitbucket, native tools etc.
4) Containerization – Docker, Kubernetes etc.
5)Orchestration – Airflow, Cloud Composer, Kubeflow etc.
6) Languages and scripting: Python, Scala Java etc
7) Cloud Services – Google Cloud Platform and native tools
8) Analytics and ML tooling – Vertex AI, Sagemaker, ML Studio
9) Execution Paradigm – low latency/Streaming, batch and micro batch processing
10)Data platforms – Big Data (Dataproc, Hadoop, Spark, Hive, Kafka etc.) and Data Warehouse (BigQuery, Teradata, Redshift, Snowflake etc.)
11)Visualization Tools – Looker, PowerBI, Tableau