AI Data Engineer

Role & responsibilities

Preferred candidate profile

Background / Qualifications

• 5+ Years in Financial Services / Financial Technology with a primary focus on loans/credit products
• Bachelors Degree in technology/related field or equivalent work experience
• 4+ Years of SQL and/or Python experience is a must
• Strong knowledge of data concepts and tools and experienced in RDMS such as MS SQL Server, Oracle etc.
• Well-versed with concepts and techniques of Business Intelligence and Data Warehousing
• Strong database designing and SQL skills. objects development, performance tuning and data analysis
• In-depth understanding of database management systems, OLAP & ETL frameworks
• Familiarity or hands on experience working with REST or SOAP APIs
• Well versed with concepts for API Management and Integration with various data sources in cloud platforms, to help with connecting to traditional SQL and new age data sources, such as Snowflake
• Familiarity with BI technologies (e.g. Microsoft Power BI, Oracle BI) is an advantage
• Hands-on experience at least in one ETL tool (SSIS, Informatica, Talend, Glue, Azure Data factory) and associated data integration principles is an advantage
• Experience of a range of debt product types - including, but not limited to, syndicated (par & distressed), bilateral, direct lending originations, commercial real estate loans – is preferred but not essential

• Minimum 1+ year experience with Cloud platform technologies (AWS/Azure), including Azure Machine Learning is desirable. Following AWS experience is a plus:

• Implementing cloud storage using Amazon S3
• Serverless approaches using AWS Lambda (e.g., AWS SAM)
• Configuring Amazon EC2 instances
• Azure Machine Learning services
• Familiarity with Docker and containerization concepts

• Exposure to CI/CD pipelines using Jenkins or GitHub Actions

• Advantageous to have below experience with AI and emerging technologies:

• Exposure to LLM frameworks such as LangChain, LlamaIndex, or OpenAI/Azure OpenAI APIs
• Familiarity with AI agent orchestration tools such as AutoGen or CrewAI
• Understanding of document intelligence tools, e.g. AWS Textract, Azure Document Intelligence
• Awareness of vector databases (e.g., Pinecone, pgvector) for knowledge base and semantic search
• Understanding of AI governance principles, e.g. auditability, explainability, human-in-loop review

Previous Work Experience:

• Experience querying databases and strong programming skills: Python, SQL, PySpark etc.
• Prior experience supporting ETL production environments & web technologies such as XML is an advatange
• Previous working experience on Azure Data Services including ADF, ADLS, Blob, Data Bricks, Hive, Python, Spark and/or features of Azure ML Studio, ML Services and ML Ops is an advantage
• Experience with dashboard and reporting applications like Qlik, Tableau, Power BI

Perks and benefits