AI Developer

Quick Facts

• Role: AI Developer (Agentic AI / RAG)

Description

Design and develop Agentic AI solutions to improve the SDLC—building and integrating AI agents that support requirements analysis, planning, development, testing, and technical quality. Implement RAG (Retrieval-Augmented Generation) with contextual search, embeddings, and integration with knowledge bases and documents. Integrate AI models and agents with applications, APIs, developer tools, and DevOps processes.

Responsibilities

• Design and build Agentic AI solutions for SDLC improvements

• Build and integrate AI agents for requirements analysis, planning, development, testing, and technical quality assurance

• Use Azure AI Foundry and Azure AI services

• Design and implement RAG solutions (contextual search, embeddings, knowledge-base/document integration)

• Integrate AI models and agents with applications, APIs, developer tools, and DevOps processes

• Design automation for CI/CD, code review, automated tests, and code quality control

• Create prototypes and Proof of Concept (PoC) and validate technical hypotheses

• Analyze AI opportunities across SDLC and identify automation potential

• Assess solution effectiveness and recommend directions for further development

• Collaborate with developers, architects, and SDLC process experts

• Participate in R&D initiatives applying AI in IT processes

Requirements

• Very good knowledge of Azure AI Foundry and Azure AI services in the Microsoft Azure ecosystem

• Practical experience designing and building Agentic AI-based solutions

• Experience designing and implementing RAG solutions (contextual search, vectorization, embeddings, knowledge-base/document integration)

• Ability to integrate AI models with applications, APIs, developer tools, and DevOps processes

• Software development experience, preferably in Java or related technologies

• Knowledge of SDLC, CI/CD, code review, automated tests, and software quality assurance practices

• Ability to rapidly prototype, validate hypotheses, and iterate in an experimental R&D approach

• Ability to independently analyze problems and propose technical solutions

• Ability to collaborate with development teams, architects, and SDLC process experts

• Ability to translate AI capabilities into concrete improvements to development processes

• Strong focus on practical, deployable solutions, prototypes, and automation

Benefits

• Opportunity to work on diverse projects in Software, Embedded, Data, and Cloud Services

• Participation in additional pre-seals and expert initiatives

• Access to medical care, a sports card, and a cafeteria platform according to partnership rules