Specialist - Data Engineering

Company:

Marsh

Description:

AI Python Engineer

We are seeking a Python engineer with approximately 3 years of experience to join Marsh AFN team. This role is well suited for a professional who demonstrates strong ownership, and is eager to grow within a fast-moving, AI-driven environment.

About the team

The Autonomous Finance (AFN) team is building the next generation of intelligent finance solutions. We work across multiple projects that combine Python engineering, AI agents, ETL pipelines, and cloud/data services to deliver innovative products for finance.

We operate in a fast-moving, collaborative environment where experimentation and continuous learning are encouraged. The team embraces AI, machine learning, and cutting-edge technology to solve complex problems and create meaningful impact.

Key responsibilities

Contribute to the development and maintenance of Python-based pipeline components

Work with AI tools, agents, and skills to support automated document extraction and workflow orchestration

Help improve ingestion, parsing, validation, and processing logic across the pipeline

Support integrations with external systems and services, including Databricks, GitHub, Azure, PostgreSQL, MongoDB, Copilot and document repositories

Assist in debugging, testing, and evaluating AI-assisted outputs

Contribute to the reliability, maintainability, and scalability of the platform

Proactively learn the business domain, technical stack, and evolving AI tooling

Communicate effectively in English with technical and non-technical stakeholders

Required qualifications

Around 3 years of professional software development experience

Strong proficiency in Python

Familiarity with AI tools, agents, and modern AI application workflows

Demonstrated ability to work independently, learn quickly, and take initiative

Strong English communication skills , both written and verbal

Bachelor’s degree in Computer Science, Software Engineering, Information Technology, Artificial Intelligence, Data Science, or a related field preferred

Preferred qualifications

Experience with LLM orchestration frameworks such as LangChain or LangGraph

Exposure to Databricks , Azure , PostgreSQL , MongoDB , or MLflow

Experience building or supporting AI-enabled applications or workflow automation systems

Familiarity with document processing, data pipelines, or production-grade automation

A proactive mindset and interest in continuous learning

Why join our team :

We help you be your best through professional development opportunities, interesting work and supportive leaders.

We foster a vibrant and inclusive culture where you can work with talented colleagues to create new solutions and have impact for colleagues, clients and communities.

Our scale enables us to provide a range of career opportunities, as well as benefits and rewards to enhance your well-being.

Marsh (NYSE: MRSH) is a global leader in risk, reinsurance and capital, people and investments, and management consulting, advising clients in 130 countries. With annual revenue of over $27 billion and more than 95,000 colleagues, Marsh helps build the confidence to thrive through the power of perspective. For more information, visit corporate.marsh.com, or follow us on LinkedIn and X.

Marsh is committed to creating a diverse, inclusive and flexible work environment. We aim to attract and retain the best people and embrace diversity of age, background, disability, ethnic origin, family duties, gender orientation or expression, marital status, nationality, parental status, personal or social status, political affiliation, race, religion and beliefs, sex/gender, sexual orientation or expression, skin color, or any other characteristic protected by applicable law.

Marsh is committed to hybrid work, which includes the flexibility of working remotely and the collaboration, connections and professional development benefits of working together in the office. All Marsh colleagues are expected to be in their local office or working onsite with clients at least three days per week. Office-based teams will identify at least one “anchor day” per week on which their full team will be together in person.