Machine Learning Engineer

Job description

At Prudential, we understand that success comes from the talent and commitment of our people. Together, we have a shared vision in securing the future of our customers and our communities. We strive to build a business that you can shape, an inclusive workplace where everyone’s ideas are valued and a culture where we can thrive together. Our people stay connected and tuned in to what’s happening around us, keeping us ahead of the curve. While focused on the long-term, we look to the future to bring growth, development and benefit to everyone whose lives we touch.

Job Description

Job Purpose:

The main purpose of the role is to go live with cutting edge AI. Only what lands in the customers’ hands, and makes lives better, is real.

The candidate will be based in the Artificial Intelligence Center of Excellence (AICoE) and work closely with the regional infrastructure and IT security teams to drive and roll out the regional initiative to deploy AI-as-a-Service deliveries. This role involves significant levels of interaction with multiple parties such as Data Scientists, Data Engineers, Security Analysts, and key stakeholders from local businesses.

Job Responsibilities:

This role will support the roll out of AI-as-a-Service deliveries across the region, including the AI model API, full stack application as well as SDK deployments.

Predominantly, the Machine Learning Engineer for AI DevOps will further enhance the existing deployment pipelines and bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, and artificial intelligence to ensure the delivery of sound processes with clear documentation, which meet the Prudential’s AI and data governance practices, IT security obligations and business unit’s efficiency expectations.

Minimum Job Requirements:

  • Experience with one or more software development programming languages like Java, C/C++, C#, Python, JavaScript, or react-native
  • Advanced experience with shell script, git and version control
  • Well versed with containerization, such as docker, docker-compose, helm and Kubernetes
  • Skilled at CICD tools, like Jenkins/Azure DevOps, Bamboo/Gitlab and Artifactory
  • Expertise in understanding and using of ML and supportive libraries: TensorFlow, PyTorch, NumPy, pandas, matplotlib, sci-kit, etc.
  • Working experience on cloud-native enterprise deployment

Desirable technical knowledge (ideally you have one or more of these):

  • Knowledge of cloud-based ML solutions: Databricks and MLflow
  • Experience in IOS and Android deployment
  • Experience with NoSQL such as MongoDB or ELK stack
  • Experience with building a data lake with data injected from various source systems to cloud service providers

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