Lead Engineer, Investment Systems

• This role cannot sponsor - open to USC or GC

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
• 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 resolution, and ontology or schema design

Education Preferred

• Bachelor of Science (B.S.) or equivalent experience
• Master of Science (M.S.) or equivalent experience

Preferred Qualifications

• 12+ years of software Engineering and delivery experience preferred
• 5+ years of technical leadership experience (as a Distinguished Engineer, technical lead, or hands-on engineering director)