Principal Quantitative Engineer - Investments Technology (Private Markets)

Description

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 work with the Portfolio Strategy & Management (PSM) team in Private Markets, driving design and implementation of small-scale applications and proofs of concept (POCs) that improve private markets portfolio analysis, develop AI-enabled workflows, and enhance reporting systems & processes across private equity, private credit, real estate, and infrastructure.

 

Working directly with the PSM team and the broader Private Markets organization, you will focus on enhancing day-to-day AI use cases, helping turn prototypes and POC artifacts into robust systems and tools that scale into production-ready applications. Typical work includes commitment pacing and cash-flow forecasting models, portfolio exposure and look-through analytics, fund and manager performance reporting (IRR, MOIC, DPI, PME), and the data pipelines that bring together internal and third-party private markets data. 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 sits within Private Markets Technology and operates under LMI’s Embedded Tactical Development (ETD) framework. The work is highly iterative: you’ll talk directly with the business, choose the tools and stack that fit the problem, and ship in short cycles without sprint planning or scrum ceremonies.

 

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.

 

Note: This Boston-based role has a hybrid work arrangement (3 days per week in office).

Hiring Manager: Patrick Devine

 

Responsibilities

• Embed with the PSM team to understand portfolio strategy, pacing, liquidity, and performance workflows, and translate them into well-scoped, iteratively delivered tools.
• Design and build full-stack Python applications, data pipelines, and decision-ready reports on enterprise platforms (GitHub, CI/CD via GitHub Actions, Snowflake, Azure AD-secured web apps).
• Help analysts take AI-generated prototypes and proofs of concept and harden them into reliable, maintainable, institutional-grade applications.
• Implement output validation, documented assumptions and limitations, and basic runtime logging for decision-support tools; provide best-effort operational support and remediate vulnerabilities within enterprise SLAs.
• Maintain clean, well-organized repositories: consistent project structure, automated linting and formatting, semantic version bumps and tagged releases, and up-to-date documentation so every solution stays maintainable and auditable.
• Contribute to and reuse shared utilities (connectors, authentication helpers, report distribution) in collaboration with other embedded engineers.

 

Qualifications

• 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 5 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 in front-end development and user experience (UX) design required; experience with Pythonic front-end and data visualization libraries (e.g., Plotly, Dash) preferred.
• Experience using Version Control (Git) required; experience with CI/CD workflows (e.g., GitHub Actions) and cloud deployment preferred.
• 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 institutional-grade, production-ready applications. Experience bridging the gap between rapid AI-assisted development and enterprise-quality software delivery 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.
• Strong SQL skills required. Experience with Snowflake is a plus.
• Familiarity with private markets data platforms (such as eFront, PitchBook, DealCloud) 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; experience with Monte Carlo or scenario-based cash-flow modeling is a plus.
• Solid understanding of private markets, including fund structures, capital calls and distributions, J-curve dynamics, commitment pacing, and performance measurement (IRR, MOIC/TVPI, DPI, PME); foundational knowledge across private equity, private credit, real estate, and infrastructure.
• Practical experience in developing and maintaining models, tools, and reports that showcase a deep understanding of quantitative techniques, methods, statistics and econometrics.
• 8+ years of experience
• Bachelor's degree or equivalent experience