Principal Front Office Engineer, Investment Innovation, Integration & Trading

Liberty Mutual Insurance•Boston, MA

22d•$128,000 - $225,000•Hybrid

About The Position

We are seeking a highly skilled and motivated Principal Front-Office Engineer to join our prestigious investment firm. As a Principal Front-Office Engineer, you will serve as the technical lead embedded within the Investment Innovation, Integration & Trading (I3T) team, driving design and implementation of small-scale proofs of concept and prototypes that support the fixed income portfolio, improve investment analysis, develop AI-enabled workflows, and enhance reporting systems & processes. You will partner closely with analysts and investment teams to enhance their day-to-day use of AI, helping them build artifacts in a more technically sound manner and building robust systems and tools that scale those prototypes into production-ready applications. We’re looking for a hands-on software architect and builder who can design systems, rapidly iterate on them, and deliver quality solutions that power analytics, reporting, and investment decision-making. This role is ideal for a Lead or Senior Engineer who thrives as an individual contributor and wants to drive technical direction without moving into management.

Requirements

Excellent problem-solving skills, with the ability to think critically, independently, and act with minimal handholding.
Effective communication skills, with the ability to clearly articulate complex ideas and analysis to both technical and non-technical stakeholders.
Strong attention to detail, organization, and the ability to manage multiple tasks and priorities in a fast-paced environment.
Full-stack development knowledge with a minimum of 8 years professional experience programming in Python demonstrating the ability to write efficient and robust code able to process and analyze large financial datasets.
Experience with key Python Libraries (pandas, NumPy) required.
Experience writing and maintaining BQL queries, leveraging PyBQL and working with the Bloomberg data ecosystem is strongly preferred.
Experience in front-end development and user experience (UX) design required.
Experience using Version Control (Git) required.
Experience using Agentic Programming tools (Github Copilot, Claude) required.
Demonstrated ability to partner with business teams to convert AI-generated artifacts (e.g., prototypes, proofs of concept) into production-ready applications.
Strong SQL skills required.
Practical experience in developing and maintaining models, tools, and reports that showcase a deep understanding of quantitative techniques, methods, statistics and econometrics.

Nice To Haves

Hands-on experience developing within Bloomberg’s BQuant environment is strongly preferred.
Experience with Pythonic front-end and data visualization libraries (e.g., Plotly, Dash) preferred.
Experience bridging the gap between rapid AI-developed prototypes to quality full-stack applications is highly valued.
Proven ability to design, build, and scale application systems in data-rich environments including custom AI tools.
Experience developing applications for investment management firms or, more broadly, financial services is a strong plus.
Familiarity with financial data platforms (such as Bloomberg, FactSet, Aladdin, eFront, Moodys), financial databases, and data manipulation techniques strongly preferred.
Experience with statistical and time-series data analysis using pythonic libraries (such as Scikit-Learn, SciPy, cvxpy) is preferred.
Solid understanding of financial markets, with foundational knowledge across fixed income, private equity, and private credit investment domains.

Responsibilities

Serve as the technical lead embedded within the Investment Innovation, Integration & Trading (I3T) team.
Drive design and implementation of small-scale proofs of concept and prototypes that support the fixed income portfolio, improve investment analysis, develop AI-enabled workflows, and enhance reporting systems & processes.
Partner closely with analysts and investment teams to enhance their day-to-day use of AI.
Build artifacts in a more technically sound manner.
Build robust systems and tools that scale prototypes into production-ready applications.
Design systems, rapidly iterate on them, and deliver quality solutions that power analytics, reporting, and investment decision-making.
Drive technical direction without moving into management.