AI/ML Engineer

MLOps Engineer

Project: The use case which requires operationalization of AI/ML driven decision making in Azure Synapse platform. There is a data science team doing the model development for 3-4 use case and need an MLOps engineer to operationalize data pipeline for the ML models in Azure Synapse environment. Below are few of the use cases:

• Intelligent Approval Agent: Provide recommendations on approvals based on the historic data
• Access Risk Review: Existing accesses and their risk recommendations
• Access Chat Bot: Use chatbots to serve simple requests on access and password resets
• Automated testing of Secure Request: Identify invalid requests and avoid unnecessary accesses being provided or else incidents created for such requests
• This is a hands-on engineering role: MLOps engineer to operationalize data pipeline for the ML models in Azure Synapse environment.

Top requirements: 1) Design, build and maintain end-to-end (Data/Dev/ML) CI/CD pipeline that automates data collection, data analysis, experimentation, model training, model serving and monitoring in production. Achieve continuous delivery of AI software solution and updates to production at scale. 2-5 year experience 2) Develop end-to-end (Data/Dev/ML) Ops pipelines based on in-depth understanding of cloud platforms, AI lifecycle, and business problems to ensure analytics solutions are delivered efficiently, predictably, and sustainably 3-5 year experience 3) Build, test and maintain the infrastructure and tools to facilitate the consistent and automated AI software solution development and release process. 2-5 year experience 4) Identify, design, and implement internal process improvements, automating manual processes, optimizing data delivery, re-designing infrastructure for greater scalability 2-5 year experience

• What experience will set candidates apart from one another? Extensive hands-on operationalization experience of AI/ML model.

Interview process: a. How many rounds? 2 rounds b. Video vs. phone? video c. How technical will the interviews be? 1 technical

Location: Open to 100% remote.

Roles and responsibilities: Build, test and maintain the infrastructure and tools to facilitate the consistent and automated AI software solution development and release process. Design, build and maintain end-to-end (Data/Dev/ML) CI/CD pipeline that automates data collection, data analysis, experimentation, model training, model serving and monitoring in production. Achieve continuous delivery of AI software solution and updates to production at scale. Develop end-to-end (Data/Dev/ML) Ops pipelines based on in-depth understanding of cloud platforms, AI lifecycle, and business problems to ensure analytics solutions are delivered efficiently, predictably, and sustainably. Collaborate with data scientists, engineers, product teams and other key stakeholders. Identify, design, and implement internal process improvements, automating manual processes, optimizing data delivery, re-designing infrastructure for greater scalability

Qualifications: DevOps, data engineering and ML background. Familiar with best practices in the data engineering and MLOps community. Experience building and maintaining CI/CD pipelines. Demonstrated experience on a broad range of DevOps tools such as Ansible, Docker, Kubernetes, Jenkins etc. Experience building, deploying and maintaining ML models in production. Experience with MLOps tools such as ModelDB, MLFlow and Kubeflow. Familiarity with python tools for data science. Experience with cloud infrastructure. Experience with Microsoft Azure Platform is a plus. Excellent communication and teamwork skills. Ability to collaborate effectively with internal business stakeholders, outside partners and technology teams.