Distinguished Engineer, Investment Systems

Job Description Summary

For over forty years, HarbourVest has been home to a committed team of professionals with an entrepreneurial spirit and a desire to deliver impactful solutions to our clients and investing partners. As our global firm grows, we continue to add individuals who seek a collaborative, open-door culture that values diversity and innovative thinking.

In our collegial environment, that’s marked by low turnover and high energy, you’ll be inspired to grow and thrive. Here, you will be encouraged to build on your strengths and acquire new skills and experiences.

We are committed to fostering an environment of inclusion that promotes mutual respect among all employees. Understanding and valuing these differences optimizes the potential of both the individual and the firm.

HarbourVest is an equal opportunity employer.

This position will be a hybrid work arrangement. You will receive 18 remote workdays per quarter to use at your discretion, subject to manager approval. For example, you may choose to work in the office 4 days per week and take one remote day weekly (typically 13 weeks per quarter), leaving 5 additional remote days to be used as needed.

Our Investment Platforms group is seeking a Distinguished Engineer to architect and guide our technical teams, to lead the design, delivery and long-term evolution of our private equity and private credit front office applications. This is a senior technical leadership role for someone who sets architectural direction and stays close to the code. You will guide how we build scalable financial technology systems and modern AI features. These features support our investment staff's decisions and improve engineering standards across all teams you impact. A core part of this role is crafting and delivering distributed financial technology solutions. You will modernize them using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and agentic systems. These solutions will be put into production using our proprietary private markets data.

This hybrid position spans both application development and artificial intelligence development, requiring equal depth in platform architecture and applied AI

The ideal candidate is:

A technical leader who can provide architectural mentorship and influence teams without relying solely on formal authority

A self-starter with a love for technology, software application delivery, AI/ML, and mathematical applications

Experienced in taking generative AI solutions from prototype to reliable, production-grade systems

Equipped with excellent interpersonal skills to work well across multiple teams

Possessed strong analytical, organizational, and problem-solving skills as well as with outstanding attention to detail

Passionate about building tools to enable private and financial investment systems

What you will do:

Be responsible for the technical architecture for our investment systems, translating investment department needs into scalable, maintainable solutions

Lead features end to end design, code, run, and maintain pipelines, transformations, views, and test suites for applications and data validation

Establish engineering standards and provide mentorship to team members on software, data, and AI engineering practices

Build and deliver sophisticated AI technologies, LLM-powered applications, RAG pipelines, and autonomous and human-in-the-loop agents grounded in HarbourVest’s proprietary data

Apply AI across the SDLC, using AI-assisted development, testing, and code review to accelerate delivery and quality

Serve as a technical point of reference, reviewing designs and working with the firm’s Architecture Review Board to champion responsible engineering and AI standards for investment technology solutions

Partner with our Platform Engineering and Quantitative Investment Science teams to align technology strategies, optimize platform capabilities, and drive investment platforms business outcomes.

What you bring:

Leadership & Domain Expertise

This is a hands-on, code-first role. You'll write production code regularly, and your architectural decisions will grow directly out of that hands-on experience.

Distinguished Engineer-level experience architecting and leading implementation of large-scale systems for investment platforms and analytics engineering teams in investment management

Set engineering standards and mentor team members on software, infra, security, data, and AI engineering practices

Partner with Data, DevOps, Security, Infrastructure, and Application Development teams to integrate automated deployment and testing.

Act as a technical point of reference by reviewing builds, resolving complex problems, and championing engineering and responsible-AI procedures

Deep understanding of data as a strategic asset, treating data quality, structure, and governance as core to the role, not a downstream concern.

Extensive experience with private equity datasets, a delivery-focused, entrepreneurial mindset, and a track record of shipping software projects optimally to production are critical

Core Engineering Skills

Proficient in Python or Java and skilled in full-stack development using TypeScript, Node with experience in CI/CD pipelines. Python is preferred

Experienced in developing scalable FastAPI-based microservices maximising GraphQL and gRPC

Strong experience in data modeling, engineering, ETL/ELT frameworks, data quality and analytics using Snowflake or equivalent cloud warehouses

Experience with modern real-time and streaming data technologies (such as Apache Kafka, Azure Event Hubs or cloud-native event streaming platforms)

Expertise in building Docker or Kubernetes (AKS or EKS) containerized applications

Experience applying AI throughout the software development lifecycle for coding, validation, and code assessment with tools such as GitHub Copilot, Codex, or Claude Code

Experience building and deploying production systems on major cloud platforms (AWS, Azure, or GCP) would be advantageous

AI Engineering:

Practical experience developing tool-integrated agentic systems using the Model Context Protocol (MCP) and frameworks such as FastMCP

Practical experience developing and launching LLM solutions, RAG architectures and agentic workflows

Experience working with extensive language understanding models including platforms such as OpenAI, Anthropic, or open-source models

Hands-on experience designing autonomous and multi-agent architectures, including task planning, tool use, memory, and multi-step reasoning, using agent orchestration frameworks (e.g., LangGraph, AutoGen, CrewAI, or comparable) and human-in-the-loop patterns for high-stakes decision workflows

Experience architecting end-to-end document ingestion pipelines, including intake, OCR, layout-aware parsing, and normalization of PDFs, Word, Excel, and scanned files, with solutions including Azure Document Intelligence or LlamaParse etc

Deep hands-on expertise building custom extraction logic with lower-level libraries (e.g., Tesseract, Docling, PyMuPDF, Camelot, Tabula)

Experience fine-tuning LLMs for domain-specific extraction tasks and integrating agentic AI workflows to automate and orchestrate extraction, validation, and structuring pipelines

Nice to have skills:

Experience with DBT, pipeline orchestration tools such as Dagster or Airflow, and Azure data tooling.

Exposure to Azure OpenAI, Azure AI Foundry / AI services, or comparable cloud AI platforms is preferred

Knowledge of financial markets, investment systems, or private markets (private equity, private credit) is a plus.

Experience with simulation-based and probabilistic modeling techniques (e.g., Monte Carlo methods) for forecasting, portfolio construction or allocation, and decision-support applications

Hands-on experience building knowledge graphs, including entity and relationship extraction, entity