We are seeking a highly skilled Senior AI & Data Engineer to join our diverse team. In this role, you will design, develop, and implement advanced machine learning data models and data-driven solutions that drive innovation and business growth! You will collaborate closely with multi-functional teams to architect scalable data pipelines and deploy AI/Agentic applications in a production environment. The ideal candidate will have a strong background in data engineering, machine learning, and cloud technologies, with a consistent track record of delivering impactful AI solutions. If you’re passionate about using AI to solve problems and crafting data-driven decision-making, contact us!

REQUIRED QUALIFICATIONS:

• Knowledge of database systems, data lakes, and NoSQL databases
• Knowledge of data warehouse concepts and architectures (e.g., Synapse & Datbricks)
• Familiarity with data quality and data modelling tools
• Proficiency in using version control systems like Git for managing codebase
• Experience with Cloud native data services such as Pyspark, Scala, Azure Datafactory and Databricks
• Proficiency in data processing frameworks and techniques such as HDFS, MapReduce, Storage formats (Avro, Parquet), Stream processing
• Experience with integrating to back-end/legacy environments
• Knowledge of AI model deployment in production environments
• Experience handing real-time data for AI Applications
• Ability to build and deploy Data Ops. And ML Ops. Pipelines in Cloud-native environments

PREFERRED QUALIFICATIONS:

• 5-7 years for Data Engineering or Software Development
• Bachelor’s or Master’s in Engineering or Data Science

• Designs, builds, and maintains reliable, efficient and scalable data infrastructure for data collection, storage, transformation, and analysis.
• Implements data orchestration pipelines, data sourcing, cleansing, augmentation, and quality control processes.
• Collaborates with business and technology partners to comprehend current and future data infrastructure requirements.
• Designs, builds and maintains scalable data solutions including data pipelines, data models, and applications for efficient and reliable data workflow; including those specifically tailored for machine learning workflows.
• Designs, implements, and maintains existing and future data platforms like data warehouses, data lakes, data lakehouses for structured and unstructured data.
• Collaborates with Data Scientists and Engineers to create features and pre-process data for ML models and move data analysis models into production.
• Designs and develops analytical tools, algorithms, data landscape modernization roadmaps, and programs to support Data Engineering activities like writing scripts and automating tasks.
• Applies different data interchange formats to meet data requirements and constantly monitors data integrity throughout the organization.
• Deploys machine learning models with existing production systems and workflows, considering compatibility with other systems, data sources, and APIs.
• Designs and promotes effective use of data querying APIs to provide easy access to organizational data sources.
• Evaluates, integrates, and manages tools and frameworks within the data engineering ecosystem, ensuring compatibility and efficiency in model development and deployment.
• Designs and promotes data versioning and lineage tracking, including transparency and traceability for data used in ML model training and inference.