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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Quantitative Developer - **Company:** HarbourVest Partners LLC. - **Location:** Boston, MA, United States - **Experience:** Expert - **Salary:** $140,000.0 - $155,000.0 - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Cloud Computing, Continuous Integration, Information Engineering, Database Queries, Software Debugging, Decision Support Systems, Information Extraction, Python (Programming Language), Machine Learning, Software Product Management, Recommender Systems, Software Tools, Search Technologies, Software Deployment, Software Engineering, Unstructured Data, Feature Engineering, Retrieval-Augmented Generation, Large Language Models, Multi-Agent Systems, Model Validation, Data Representation, Backend, Information Technology, Machine Learning Operations, Virtual Agents, Software Version Control - **Published:** June 10, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=f1d41ffc4275f559 ## About the Role Do you have experience in Python?, Do you have a Bachelor's degree?, * Strong software engineering fundamentals and a production-oriented machine learning mindset * A practical interest in using ML and agentic AI to improve investment research, data quality, decision support, and workflow scale * Healthy skepticism about model outputs, with strong instincts for evaluation, backtesting, monitoring, and human review * Comfort turning ambiguous analytical workflows into measurable, maintainable production systems * Strong collaboration skills across quant developers, data engineering, product, and investment stakeholders * Curiosity about finance, private markets, and the data problems behind investment decision-making, * Experience with applied machine learning, including feature engineering, model training, evaluation, inference, and monitoring * Ability to learn and apply the right ML, statistical, and data engineering tools for the problem, with sound judgment around model choice, data representation, reproducibility, and production constraints * Strong SQL skills and comfort designing data models for analytical or product-facing systems * Experience building production services, APIs, batch jobs, queues, or scheduled pipelines around data-intensive workflows * Practical experience with embeddings, semantic search, ranking, recommendation systems, information extraction, agentic AI systems, or LLM-enabled workflows * Familiarity with agent patterns such as tool use, retrieval-augmented generation, planning, memory, workflow orchestration, and structured human review * Strong testing habits and ability to debug model behavior using real data, logs, metrics, and user feedback * Ability to explain model behavior, data limitations, quality tradeoffs, and operational risk to technical and non-technical partners * Familiarity with cloud platforms, containerized development, CI/CD, observability, and secure production deployment patterns * Preferred: experience with financial data, time series data, private markets workflows, vector databases, agent frameworks, unstructured data processing, feature stores, model registries, or multi-tenant enterprise systems Education Preferred Bachelor of Science (B.S.) or Master's in Computer Science, Machine Learning, Statistics, Mathematics, Engineering, or equivalent experience, 5-8 years software development experience, with significant production experience in machine learning engineering, data-intensive backend systems, search/ranking systems, quantitative software, agentic AI systems, or applied AI product development ## Description * Build and productionize ML models, feature pipelines, and inference workflows for QIS applications * Develop semantic matching, ranking, recommendation, and peer-selection systems for funds, managers, deals, companies, and comparable opportunities * Build unstructured data intelligence, classification, enrichment, and AI-assisted review workflows for complex internal materials and operational datasets * Design agentic AI workflows that can plan multi-step analyses, call internal tools, retrieve relevant context, and produce traceable recommendations for human review * Create evaluation frameworks for AI agents, including task success metrics, regression suites, prompt/version tracking, guardrail tests, and failure-mode analysis * Create model evaluation harnesses, benchmark datasets, backtests, monitoring, drift detection, and quality gates so ML outputs can be measured and trusted * Integrate embeddings, retrieval, model-serving APIs, agent orchestration, batch jobs, and human-in-the-loop review controls into existing QIS tools * Partner with data and platform engineers to make ML workflows repeatable, observable, secure, and easy to operate * Establish practical MLOps patterns for experiment tracking, model versioning, deployment, rollback, audit trails, and production support * Translate investment workflow needs into pragmatic ML solutions while being clear about limitations, confidence, and operational risk ## 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