Job Summary
As a Principal AI Engineer, you will play a critical role in the design, development, and deployment of AI platform. You will work collaboratively with a cross-functional team, focusing on MLOps, machine learning standardization, GPU and cloud platform setup, and data integrations. Your expertise will be pivotal in ensuring the scalability, reliability, and efficiency of our AI solutions
Key Responsibilities
• Standardization: Establish and enforce best practices for machine learning model development, testing, and deployment.
• Data Integration: Design and implement robust data integration pipelines to ensure seamless access to diverse data sources.
• Feature Store: Developing and maintaining a feature store for data features.
• EDA (Exploratory Data Analysis): Implementing and maintaining tools for exploratory data analysis.
• Enterprise VectorDB and Search Engine: Building and maintaining enterprise-level vector databases and search engines.
• API Interfaces and Gateway Integrations: Developing and maintaining API interfaces and gateways for seamless integrations.
• ETL Templates: Creating and maintaining templates for Extract, Transform, Load (ETL) processes.
• GDP Integration: Ensuring integration with Global data platform to feed data into AI feature stores.
• External Data Sourcing: Managing the sourcing and integration of external data.
• IDAM Integrations: Implementing and maintaining Identity and Access Management (IDAM) integrations.
• Onboarding Application for Users: Developing and maintaining applications for user onboarding, financial operations (FinOps), IT asset management (ITAM), and process integrations.
• Collaboration: Work closely with data scientists, software engineers, and platform engineers to deliver high-quality AI solutions.
• Innovation: Stay abreast of the latest advancements in AI and machine learning, and apply this knowledge to improve our platform and processes.
• Mentorship: Provide technical guidance and mentorship to junior engineers and team members.
Qualifications
• Experience: 3-5 years of experience in AI and machine learning engineering, with a proven track record of delivering complex AI projects.
• Technical Expertise: Strong proficiency in machine learning frameworks (e.g., TensorFlow, PyTorch), MLOps tools (e.g., MLflow, Kubeflow), and cloud platforms and infrastructure as code (e.g., AWS, Azure).
• Programming Skills: Proficiency in programming languages such as Python, Java, or C++.
• Data Engineering: Strong skills in data engineering, including data integration, ETL processes, and working with large datasets.
• Problem-Solving: Excellent analytical and problem-solving skills, with the ability to think critically and creatively.
• Communication: Strong interpersonal and communication skills, with the ability to work effectively in a collaborative team environment.