Jamal Martin

About

Senior Software and AI Engineer delivering production-grade LLM systems end-to-end, specializing in Retrieval-Augmented Generation (RAG), semantic search, and autonomous/multi-agent frameworks. Strong full-stack fundamentals across React, Node.js, Go, and Python, with a track record of building scalable microservices, REST APIs, and AI guardrails in high-volume, data-intensive environments.

Experience

• Senior AI Engineer · Mindtree06-01-2024

  Architected an AI-powered land intelligence platform using cloud-native microservices with Go, Python, React, PostgreSQL, and Microsoft Azure, processing a substantial volume of property listings daily while maintaining 99.95% uptime. Created semantic search and recommendation capabilities using OpenAI API models and vector databases, enabling natural language property discovery and improving search relevance meaningfully across the platform. Developed document intelligence pipelines with Python, spaCy, and custom named-entity recognition (NER) models to automatically extract planning regulations, property metadata, and regulatory details, cutting manual processing effort noticeably. Launched an LLM-powered assistant that helps users understand planning constraints, evaluate opportunities, generate reports, and answer property questions, reducing support ticket volume substantially. Engineered reverse-search functionality allowing businesses to specify requirements and receive ranked location recommendations based on demand, feasibility, competition, demographics, and infrastructure. Benchmarked and selected between Generative Pre-trained Transformer 4 (GPT-4), Claude, and Gemini based on cost, latency, and quality tradeoffs, lowering monthly API expenses noticeably while maintaining recommendation accuracy. Delivered an autonomous agent framework that analyzes property development potential across multiple jurisdictions, cutting feasibility assessment time from days to minutes for investors and developers.

• Senior Software Engineer · Primerica07-01-2022 – 05-31-2024

  Engineered agent frameworks and autonomous systems for customer-facing financial features using LangChain and function calling, handling substantially higher query volume during peak tax season. Shipped LLM-powered features into an existing React and Node.js codebase, integrating with REST APIs and microservices while maintaining strict latency Service Level Agreements (SLAs) below acceptable thresholds. Developed responsive frontend components in React for AI-assisted financial document analysis, streamlining user workflows and reducing manual data entry across thousands of daily sessions. Implemented semantic search pipelines with ChromaDB and embedding models, speeding up policy document retrieval meaningfully for internal support representatives. Reduced token usage and processing costs through dynamic prompt engineering and context window management across multiple LLM providers serving insurance and investment content. Monitored production AI systems with custom logging and dashboards, cutting troubleshooting time substantially for issues related to model drift and output quality.

• Software Engineer · PokerGo08-01-2019 – 06-30-2022

  Built and maintained full-stack features using React, Node.js, TypeScript, and PostgreSQL, delivering consistent performance for thousands of concurrent users. Containerized microservices with Docker and orchestrated deployments on Kubernetes, improving environment consistency and rollback speed across staging and production.

• Software Engineer · PokerGo09-01-2013 – 05-31-2017

  Configured Continuous Integration and Continuous Deployment (CI/CD) pipelines with GitHub Actions and automated integration tests, reducing release cycle time noticeably while catching regressions earlier. Integrated third-party AI APIs for basic content moderation and summarization, gaining practical exposure to prompt design and asynchronous response handling. Refactored legacy REST endpoints into scalable microservices, lowering average response time across several internal APIs. Learned production best practices for observability and incident response at scale, including error budgets and structured logging.

Education

• Wayne State UniversityBachelor of Science (B.S.), Computer Science

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