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Rate 5 600 € - 6 400 € / month

Form of cooperation Full-time

Sector Insurance

Location 100% Remote

The reward is calculated upon delivery of 20 MD per month (1MD=8h)

Project duration 6-12 months with possibility of extension

Period of cooperation 01.10.2026 - 30.09.2027

Start date ASAP or by agreement

Project duration 6-12 months with possibility of extension

Period of cooperation 01.10.2026 - 30.09.2027

Start date ASAP or by agreement

Technology

AWS Amazon Web Services
MS Azure
Python
LLM Large Language Model
Claude
Azure Machine Learning
Agentic AI
AI Automation
MCP (Model Context Protocol)

Languages

Project description

identification, design, and implementation of AI scenarios and automations from detailed process understanding, through prototyping and iterative refinement, to production deployment [Forward Deployed Engineering]
leading structured meetings with end-users (operations, claims handling, risk underwriting, administrative teams) to understand the real process flow, including exceptions, manual procedures, and decision rules
transformation of business needs and processes into a technical specification – defining process steps, decision criteria, data sources, exceptional situations, and success metrics
design and development of prototypes and PoCs, presenting them to end-users, gathering feedback, and subsequently iteratively improving the solutions
design and implementation of AI/GenAI solutions for process automation, especially:
- data extraction and processing from documents and PDFs
- automated form filling
- case classification and routing
- decision support
- document summarization and transformation
development of agent-based AI solutions using Prompt, Context, and Harness Engineering and concepts of MCP / Agent-to-Agent
integration of AI solutions with internal systems via APIs
development of backend services and microservices in Python
utilizing the Microsoft Azure ecosystem and Amazon Bedrock in the design and implementation of cloud AI solutions
deployment of successful prototypes into production with an emphasis on scalability, monitoring, and long-term maintainability
evaluation of AI/LLM solution quality and performance – creation of golden datasets, regression testing for prompt changes, and monitoring of quality, latency, and costs
adherence to the requirements of a regulated environment, especially GDPR, personal and sensitive data protection (PII) rules, AI model governance, and principles prohibiting the use of data for model training
collaboration possible only on a full-time, 100% REMOTE basis with occasional ad-hoc on-site presence (Nitra)
final remuneration depends on candidate's experience and the selection process

Project requirements

project experience in designing and implementing AI, GenAI or process automation solutions [Forward Deployed Engineering]
advanced knowledge:
- Python
- design and development of microservices
- API integrations
- Microsoft Azure ecosystem
- principles of software engineering and TDD
advanced experience with:
- GenAI / LLM solutions and their application in business process automation
- Prompt, Context, and Harness Engineering
- gathering and refining requirements directly from end-users
- transforming business processes and requirements into a technically feasible specification
- designing PoCs and prototypes, iteratively improving them based on feedback, and subsequently deploying them to production
- designing scalable, monitorable, and long-term maintainable production solutions
- evaluating the quality and performance of LLM/AI systems, including creating golden datasets, regression testing, and monitoring quality, latency, and costs
- ability to clearly explain technical concepts to non-technical users while also engaging in expert-level technical discussions
ability to connect business, user, and technical perspectives
experimental approach and ability to continuously and iteratively improve solutions
excellent communication skills and ability to lead meetings
active knowledge of English at a communicative level (min. B2+)
experience with the following is an advantage:
- Amazon Bedrock
- concepts of MCP (Model Context Protocol) / Agent-to-Agent
- agent-based AI systems and AI solutions utilizing external tools and services
- automated document and PDF processing
- GDPR, personal data protection (PII), and AI model governance
- projects in the insurance or financial sector
- knowledge of German
high level of autonomy and responsibility for the outcome
seniority and assertiveness in communication with stakeholders
pragmatic approach and focus on real results and quickly achievable benefits
strong analytical skills and ability to effectively solve problems

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