Careers · Open role
Applied AI, Data & Automation Engineer
Develop AI-assisted extraction, classification, summarization, analytics, automation, and decision-support components for operational workflows.
TYSAPA is looking for an Applied AI, Data & Automation Engineer to build the intelligence layer of its platforms and client solutions. This role focuses on practical AI and data systems that improve real workflows: document extraction, classification, summarization, routing, prioritization, dashboards, anomaly signals, forecasting, and decision support.
This is not a research-only role and not a role for building AI demos. The goal is to apply AI carefully inside operational systems, with validation, human review, confidence checks, and clear business purpose. The successful candidate should understand that AI is useful only when it improves accuracy, speed, visibility, quality, or decision-making.
The role combines Python, data processing, AI/ML, LLMs, automation logic, APIs, and practical integration with software platforms.
Role & responsibilities
- Build AI-assisted workflows for document extraction, classification, summarization, routing, and prioritization.
- Develop pipelines for invoices, forms, contracts, reports, meeting notes, customer messages, and operational records.
- Work with OCR, NLP, LLMs, embeddings, RAG, structured extraction, predictive analytics, and anomaly detection.
- Convert unstructured or semi-structured information into usable data for workflows, dashboards, and decision support.
- Design confidence scoring, validation steps, human-in-the-loop review, exception handling, and quality checks.
- Evaluate AI outputs for accuracy, reliability, usefulness, hallucination risk, and operational risk.
- Integrate AI/data services with backend systems, APIs, databases, dashboards, and workflow platforms.
- Support forecasting, prioritization, operational analytics, and decision intelligence use cases.
- Document model assumptions, limitations, data requirements, performance, and governance considerations.
- Work with product and engineering teams to decide when AI is appropriate and when simpler automation is better.
Requirements
- Strong Python skills and experience with data processing, machine learning, NLP, LLMs, RAG, or automation.
- Experience with OCR, document AI, text extraction, classification, summarization, embeddings, or structured information extraction.
- Familiarity with APIs, databases, data pipelines, backend integration, and production workflows.
- Understanding of model evaluation, validation, accuracy measurement, confidence thresholds, and human review.
- Ability to work with messy operational data and convert it into structured, useful outputs.
- Experience with dashboards, forecasting, anomaly detection, decision support, or optimization is a strong advantage.
- Familiarity with OpenAI APIs, local LLMs, vector databases, LangChain/LlamaIndex-style tools, or MLOps practices is helpful.
- Strong documentation skills and practical judgment about when AI should or should not be used.
- MSc/PhD or strong applied experience in computer science, data science, engineering, physics, mathematics, AI, or a related field is preferred.
Benefits
- Work on applied AI systems connected to real operational problems.
- Opportunity to combine AI, data, automation, document intelligence, and software implementation.
- Collaboration with a science-driven team that values evidence, evaluation, and practical usefulness.
- Exposure to projects involving document workflows, analytics dashboards, workflow automation, and decision support.
- Flexible collaboration model depending on project scope and candidate expertise.
- A responsible AI environment where human review, governance, and quality checks matter.
Ready to apply?
Applications are submitted through our secure web form only—not by email.