Knowledge
• Strong proficiency in Python, SQL, VS Code, GitHub Copilot, and data integration tools (e.g., SQL, Pyspark, experience with cloud platforms (Azure, AWS, GCP) and container architecture.
• Hands-on experience building LLM / GenAI applications production grade AI patterns like MCP, Agentic AI, RAG, NLP2SQL etc.
• Solid SQL and PostgreSQL skills; experience with PGVector or a comparable vector database (Pinecone, Neo4j etc.).
• Comfort designing and consuming REST APIs; understanding of authentication, rate limiting, and error handling.
• Working knowledge of at least one major model provider API—Anthropic (Claude) and/or OpenAI (GPT)—including prompt design, function/tool calling, and streaming.
• Experience with AI/ML frameworks and AI concepts.
• Familiarity with financial data providers (e.g., Bloomberg, Refinitiv, Factset etc.).
• Deep understanding of and asset management domain, data governance, regulatory compliance, and risk management in banking.
• Excellent communication and stakeholder management skills.
• Understanding of SQL, Oracle, Exadata, Snowflake, Data Bricks, etc…
• Understanding of development Languages: Python , .NET/C#, React, and Java
• Understanding of operating systems: Unix/BASH Shell, Windows, Linux OS
• Proficient in generative AI and LLM data preparation for financial use cases.
Experience
• Bachelor’s or Master’s degree in Computer Science, Data Engineering, or related field.
• 10+ years of experience in data engineering or integration, preferably in financial services or banking.