Compensation: $25k – $50k • 0.0% – 1.0%
We are looking for a highly technical AI Engineer with strong full-stack capabilities to architect and build the foundational AI infrastructure and core application layers for our next-generation Smart Audit platform. In this role, you will hold end-to-end ownership of the AI lifecycle moving beyond third-party API orchestration to design, train, and deploy proprietary machine learning models natively, while ensuring they are seamlessly integrated into a robust, scalable enterprise web application.
You will transform how the enterprise analyzes financial records, contracts, and operational data, turning manual, error-prone sampling into automated, 100% population risk scanning. If you are passionate about custom architecture, enterprise scalability, and deploying high-performance systems and full-stack applications to production, this role is for you.
Key Responsibilities
- Proprietary Model Architecture and Training: Design, train, and fine-tune domain-specific LLMs and deep learning models for anomaly detection, fraud identification, and automated compliance checking.
- Full-Stack Application Integration (MERN): Architect and develop the core platform features using the MERN stack (MongoDB, Express.js, React, Node.js), ensuring smooth, low-latency communication between the AI backend services and the user-facing dashboard.
- Custom GenAI and Retrieval Infrastructure: Engineer advanced, low-latency retrieval systems, custom embedding models, and multi-agent workflows optimized for massive volumes of unstructured legal and financial data, independent of high-level orchestration frameworks.
- Enterprise Production and MLOps: Establish automated, end-to-end MLOps pipelines covering data labeling, continuous training, version control, and high-throughput/low-latency model serving in production environments.
- Data Architecture Engineering: Architect custom ETL and data ingestion pipelines using Node.js/Express capable of parsing and structuring complex, multi-format enterprise data (unstructured PDFs, massive Excel sheets, ERP system dumps) into MongoDB and vector databases.
- Evaluation and Robustness Frameworks: Implement deterministic evaluation frameworks to strictly benchmark model accuracy, mitigate hallucinations, and minimize false positives/negatives in a high-stakes auditing environment.
- Security, Privacy and Explainability: Architect systems to comply with strict enterprise data isolation and privacy standards (e.g., GDPR, SOC2). Ensure the React interface provides clear, intuitive, and explainable lineage so human auditors can verify the AI’s logical reasoning.
Technical Skills and Qualifications
Required
- Experience: Demonstrated track record of building, scaling, and maintaining both machine learning models and production-grade full-stack web applications.
- MERN Stack Proficiency: Production-grade development experience with MongoDB (aggregation pipelines, indexing, schema design), Express.js, React.js (state management, hooks), and Node.js (asynchronous programming, API development).
- Deep Learning and Frameworks: Professional proficiency with Python and deep learning frameworks (such as PyTorch or TensorFlow) along with a strong understanding of neural network architectures.
Preferred (Nice-to-Have)
- Experience building, fine-tuning, or aligning open-source LLMs (e.g., Llama, Mistral) using PEFT, LoRA/QLoRA, or DPO.
- Experience designing and optimizing distributed Vector Databases (e.g., Qdrant, Milvus, pgvector) alongside traditional document stores like MongoDB.
- Expertise in customized NLP, Named Entity Recognition (NER), or building custom OCR/Document parsing pipelines.
- Experience building and scaling AI-driven full-stack solutions or multi-agent orchestrations within Enterprise SaaS, Fintech, or Legaltech.
- Experience with real-time data streaming (e.g., WebSockets, Kafka) to push live AI inference results to a frontend dashboard.
Soft Skills and Culture Fit
- First-Principles Thinker: You look past black-box solutions; you understand how a model behaves at the architectural level and how the application layer interacts with it.
- System Ownership: Comfortable owning the technical roadmaps—from the database layout and backend APIs to the AI models and the final user interface.
- Pragmatic Innovator: Ability to balance cutting-edge AI agent research with the practical realities of enterprise engineering constraints (cost, latency, UI/UX clarity, and security).