Company DescriptionProjectMark is an all-in-one CRM and proposal platform built specifically for the construction industry.
We provide companies with all the tools necessary to win the next job – industry specific pipeline & contact management, a digital asset library for proposal integrationRole DescriptionWe're looking for an AI/ML Engineer to build the next generation of AI tooling on top of our CRM platform at ProjectMark .
You'll play a critical role in designing intelligent agents and workflows that enhance how construction professionals manage their work, relationships, and data.
From intelligent opportunity scoring to auto-completing forms and surfacing insights, you'll help us bring powerful, purpose-built AI into our customers' daily experience.We are seeking a highly motivated candidate who is enthusiastic about diving into new challenges.
The ideal candidate will have a strong work ethic, be ready to invest the necessary hours, and be fully dedicated to contributing to the success of the team.
If you're passionate about solving meaningful problems and eager to make an impact, we warmly invite you to join us at ProjectMark.
Your drive and commitment will be valued and celebrated as we build something extraordinary together.ResponsibilitiesDesign, develop, and deploy machine learning models—both LLMs and smaller task-specific models—to power intelligent features within the ProjectMark CRMBuild agentic workflows that automate internal tasks and enhance user productivity through contextual decision-makingFine-tune, train, and evaluate models on proprietary datasets, applying methods like LoRA, PEFT, or instruction tuning where appropriateArchitect retrieval-augmented systems (RAG) using vector databases and embedding models for dynamic context injectionImplement intelligent agents using prompt chaining, memory systems, and external tool integrationsCollaborate cross-functionally with Product, Engineering, and Customer Success to uncover impactful AI use cases across the platformContinuously monitor, evaluate, and optimize model performance using telemetry and user feedbackEnsure scalability, security, and reliability of deployed ML systems within our broader infrastructureStay current with advancements in machine learning, agent frameworks, and model training techniques—bringing fresh ideas into productionContribute to the long-term AI roadmap, helping shape strategy while driving hands-on implementationRequirements8+ years in backend/software engineering and 3+ years of applied machine learning experienceProven experience training and fine-tuning machine learning models, not limited to LLMsStrong grasp of ML fundamentals: supervised/unsupervised learning, model selection, overfitting/underfitting, metrics, etc.Proficiency in Python and deep learning frameworks such as PyTorch, TensorFlow, or JAXExperience working with domain-specific datasets to train or adapt models to unique contextsFamiliarity with transformer-based models, embeddings, and techniques like LoRA, PEFT, and instruction tuningHands-on experience with vector databases (e.g., Pinecone, Weaviate, FAISS) and embedding pipelinesKnowledge of agentic design principles, including autonomous/multi-agent workflows and tool useAbility to build and maintain robust ML infrastructure (e.g., training pipelines, evaluation loops, inference services)Bonus: Exposure to MLOps, model deployment, or building internal tooling for AI/ML teams