Data Engineer (AI)

TL;DR

Data Engineer (AI): Designing and developing scalable data solutions and AI-driven platforms for corporate financial planning and risk management with an accent on RAG systems, knowledge graphs, and large-scale data pipelines. Focus on implementing agentic frameworks, optimizing distributed search platforms, and enhancing enterprise-wide decision-support tools.

Location: New York, NY, United States (Onsite)

Salary: USD 115,000 - 180,000

Company

A leading global investment banking, securities, and investment management firm founded in 1869.

What you will do

• Design, develop, and maintain end-to-end software and data solutions for financial planning, expense management, and risk platforms.
• Build and maintain knowledge graphs and RAG systems to enable advanced document and data retrieval, querying, and searching.
• Leverage AI tools, prompt engineering, and agentic frameworks to accelerate development and improve platform capabilities.
• Collaborate globally with sponsors and engineering colleagues to create solutions meeting complex business requirements.
• Establish governance frameworks for AI including logging, explainability, and auditability to ensure reliability.
• Take technical ownership of features and components while conducting code reviews to ensure engineering best practices.

Requirements

• Bachelor's or Master's degree in Computer Science, Data Engineering, or a similar field.
• 3+ years of proficiency in programming languages (Java, Python etc) to solve data science problems.
• Experience using Pandas, NumPy, PySpark, TensorFlow, or PyTorch to build scalable data pipelines and perform data modeling.
• Foundational understanding of AI concepts, including LLMs, prompt engineering, and retrieval-augmented generation (RAG).
• Strong analytical skills with expertise in algorithms, data structures, and software design.
• Must be located in or able to work from New York, NY.

Nice to have

• Experience with GenAI, vector databases, embedding models, and frameworks like LangChain.
• Experience optimizing distributed search platforms such as ElasticSearch or OpenSearch.
• Knowledge of MLOps practices (CI/CD for ML) and cloud-native solutions, preferably AWS.
• Familiarity with the financial industry, specifically corporate planning, expense management, or risk functions.

Culture & Benefits

• Work on high-impact platforms that directly influence firm-wide financial planning and operational resilience.
• Access to modern cloud-native architectures and AI-driven productivity tools like Copilot and Claude Code.
• Collaborative global team environment with access to experts and professional growth opportunities.
• Commitment to diversity, inclusion, wellness programs, and personal finance offerings.