Description

Key Responsibilities

Collaborate with business users and cross-functional teams to understand processes, identify opportunities, and translate requirements into technical solutions.

Design, develop, and deploy Generative AI applications using Python, LLM APIs, agent frameworks, retrieval-augmented generation (RAG), and workflow orchestration technologies.

Build AI-powered solutions for knowledge retrieval, workflow automation, document analysis, reporting, and decision-support use cases.

Develop and maintain data retrieval pipelines across structured and unstructured enterprise data sources.

Apply prompt engineering and context management techniques to improve model performance, reliability, and user adoption.

Create evaluation frameworks to assess output quality, accuracy, relevance, and compliance requirements.

Ensure solutions align with enterprise standards for security, privacy, governance, and auditability.

Communicate technical concepts, trade-offs, and implementation strategies to both technical and non-technical stakeholders.

Support continuous improvement through user feedback, testing, monitoring, and iterative enhancements.

Identify reusable components and best practices that can scale across multiple business initiatives.

Required Qualifications

Bachelor's degree in Computer Science, Engineering, Mathematics, Finance, or a related field, or equivalent practical experience.

Strong hands-on software development experience with Python.

Experience building and deploying applications using Generative AI technologies, LLM APIs, agent frameworks, and related AI development tools.

Knowledge of prompt engineering, grounding techniques, context management, testing, and output validation.

Experience working with APIs, databases, cloud services, and enterprise data platforms.

Proven ability to gather requirements, solve complex business problems, and deliver practical technical solutions.

Strong communication and stakeholder management skills.

Experience working in Agile development environments and delivering solutions from proof of concept to production.

Preferred Qualifications

Experience within financial services, capital markets, trading, risk, operations, or banking technology environments.

Familiarity with fixed income products and related business processes.

Hands-on experience with:

Retrieval-Augmented Generation (RAG)

Vector databases

Embeddings

Semantic search

Document intelligence solutions

Knowledge of AI governance, model risk management, data privacy, and compliance frameworks.

Experience developing AI evaluation methodologies, quality metrics, and performance monitoring processes.

Background in client-facing engineering, consulting, technical delivery, solution engineering, or forward-deployed engineering roles.

Exposure to cloud platforms, secure APIs, authentication, authorization, and enterprise integration patterns

Key Skills

Education

Bachelor’s degree