Highlights
Hands-on experience with Azure, GenAI, and data pipelines.
Description
Job Summary
pWe are looking for a Data Science & AI Engineer to join our team. You will be involved in developing and implementing data-driven solutions using Azure, GenAI, and other advanced technologies. This role requires hands-on experience with data pipelines, analytics, and AI features.
Responsibilities
- Develop and maintain data pipelines for ingesting data from APIs, databases, and flat files
- Implement ETL/ELT processes using Python and SQL
- Perform data cleaning, transformation, and validation
- Support integration of data across internal and external systems
- Assist in maintaining data workflows and troubleshooting data issues
- Support development of reports, dashboards, and analytical datasets
- Collaborate with stakeholders to understand reporting and data needs
- Support development of AI-powered application features
- Integrate with pre-built LLM APIs and AI services
- Test and evaluate AI outputs for accuracy and performance
Required Skills
- Data Engineering & Processing
- Data Analysis & Reporting
- Azure Cloud Support
- Python Programming
- SQL Query Optimization
Required Skills Explained
- Python and SQL: Essential for developing and maintaining data pipelines, performing data cleaning, transformation, and validation.
- ETL/ELT Processes: Key for integrating data from various sources into a unified system.
- Azure and Cloud Platforms: Important for deploying and managing AI workloads in a cloud environment.
- Data Analysis & Reporting: Crucial for supporting the development of reports, dashboards, and analytical datasets.
- AI/ML Implementation: Vital for integrating AI-powered application features and testing their accuracy.
Who is this for
pThis role is ideal for individuals with a strong background in data engineering, AI development, and cloud technologies. You should be able to work effectively in a team environment and have the ability to solve complex problems.
Why This Job is a Good Opportunity
ulliHands-on Experience: Gain practical experience in building data pipelines, analytics, and AI-enabled features.liCollaboration: Work closely with cross-functional teams to support the development of innovative solutions.liLearning & Development: Continuously learn and adopt best practices in AI, data engineering, and software development.liExposure to Advanced Technologies: Get exposure to advanced technologies like Azure, GenAI, and data decisioning systems.
Interview Preparation Tips
- Understand the Core Responsibilities: Familiarize yourself with key tasks such as data engineering, analysis, AI implementation, and application support.
- Technical Skills Review: Refresh your knowledge of Python, SQL, and Azure concepts.
- Cover Data Engineering & Processing: Be ready to discuss ETL/ELT processes and data cleaning techniques.
- Data Analysis & Reporting: Prepare examples of how you have performed exploratory data analysis and created reports or dashboards.
- AI / ML Implementation: Highlight any experience with AI-powered applications and experimentation.
Career Growth in This Role
pThis role offers a pathway to broader technical responsibilities. You'll start by contributing to building, testing, and integrating data and AI components. Over time, you can take on more leadership roles and move towards managing larger projects or leading smaller teams within the engineering department.pThe exposure to enterprise systems, cloud platforms like Azure, and AI tools will help you grow your skill set and prepare for future career advancements in the tech industry.
Explore More Opportunities
Skills
Frequently Asked Questions
What kind of data pipelines will I be working with?You will develop and maintain pipelines for ingesting data from various sources including APIs, databases, and flat files.
Is experience with AI/ML necessary?While not mandatory, familiarity with AI/ML concepts is highly beneficial as you will be integrating AI-powered features into applications.
What cloud platforms are used in this role?This position primarily involves working with Azure and other cloud-native technologies.
