Summary
What you’ll impact
The AI Solutions Engineer will design and implement AI/ML, NLP, and LLM-based solutions with a focus on secure data handling and document processing. The role requires full‑stack development skills and experience building human‑in‑the‑loop and agentic AI workflows for regulated environments.
Responsibilities
What you'll do
- Develop AI/ML solutions using NLP, LLMs, and Generative AI.
- Build LLM-based document understanding and extraction solutions.
- Work with prompt engineering, RAG, and structured/JSON output extraction.
- Use frameworks such as spaCy, Hugging Face Transformers, and LangChain or equivalent tools.
- Perform model evaluation using precision, recall, confidence scoring, and calibration.
- Build human-in-the-loop (HITL) validation workflows.
- Develop hybrid systems combining rules engines with ML/probabilistic scoring.
- Experience with document extraction and OCR technologies such as:
- Tesseract
- AWS Textract
- Azure Document Intelligence
- PyMuPDF
- python-docx
- Apache Tika
- Design PII de-identification, tokenization, anonymization, and pseudonymization solutions.
- Experience with format-preserving encryption, salted hashing, and reversible pseudonymization.
- Work with secure key-management technologies such as:
- HashiCorp Vault
- AWS KMS
- Azure Key Vault
- On-premise equivalents
- Understand Data Loss Prevention (DLP) and secure data movement between on-premise systems and AI/LLM environments.
- Backend: Python with FastAPI, Django, or Flask; Java/C# experience is also acceptable.
- Strong understanding of RESTful API development.
- Frontend: React, Angular, or Vue.
- Database: PostgreSQL, SQL Server, or similar relational databases.
- Experience designing audit-log and tracking schemas.
- Search/Retrieval: Elasticsearch or OpenSearch.
- Version Control & CI/CD: Git, Jenkins, GitHub Actions, or Azure DevOps.
- Experience developing agentic AI or multi-step LLM workflows.
- Understanding of AI orchestration, tool calling, workflow automation, and multi-step reasoning processes.
Requirements
What you’ll bring
- Bachelor's degree in Computer Science, Data Science, Software Engineering, or a related field, or equivalent experience.
- 5+ years of professional experience in AI/ML engineering.
- Strong experience with NLP-based extraction and classification.
- Demonstrated experience designing on-premise PII de-identification/tokenization pipelines.
- Full-stack development experience covering both frontend applications and backend/API architecture.
- Experience building confidence-scoring, explainable-AI, or human-in-the-loop systems.
- Hands-on experience with LLMs, RAG, and/or agentic AI workflows.
- Strong understanding of secure data architecture and PII protection.