NTT DATA Business Solutions combines the power, global competencies and sectoral expertise of NTT DATA Group, one of the world's top 10 IT solution providers with 190 thousand employees, with local experience. The company, which realizes more than 30 thousand projects worldwide every year with more than 10 thousand employees in more than 30 countries and has been transforming trust into value for 34 years, has been operating in Turkey since 2000.

As a technology service provider that draws its strength from innovation and its team of 2,500 experts in Turkey, it supports end-to-end digital transformation in business processes with its products, services and solutions. It offers technology solutions to its customers in cooperation with many different organizations, including SAP and Microsoft, the pioneers of their sectors. With the special products it develops on a global and local basis, it offers solutions to the specific needs of organizations and sectors, and provides application development services. NTT DATA Business Solutions Turkey, which extends its country-based technology projects regionally and globally, offers managed services in areas such as support, technology modernization, application management and outsourcing.

Growing its customers' businesses with technology, NTT DATA Business Solutions Turkey develops next-generation technologies for a sustainable future through its innovation center and R&D Center approved by the Ministry of Industry. It produces innovation in line with the needs of organizations in Turkey and transforms business ideas into products by working on new technologies in the fields of sustainability, artificial intelligence, IoT, blockchain and robotic process automation on cloud platforms. With more than 400 expert consultants in the Customer Experience and Support Center, it provides 24/7 application support services to hundreds of organizations on 5 continents.

We are looking for a Senior Software Architect with at least 5-8 years of total software engineering experience to design and manage the entire system architecture with a focus on LLM and Agentic workflows. The candidate is expected to have deep expertise in the Python ecosystem and a strong command of GCP (Google Cloud Platform) services.

• LLM and AI Agent Integration: Designing systems powered by Large Language Models (LLMs); specifically establishing architectures using MCP (Model Context Protocol) to enable agents to interact with data sources and tools in a secure and standardized manner.
• Python and Framework Expertise: Deep expertise in Python; building high-performance backend architectures using FastAPI (priority for AI services), Django, and Flask, with a focus on asynchronous programming.
• GCP (Google Cloud Platform) Dominance: Architecting and managing the core infrastructure on GCP; possessing advanced proficiency in Vertex AI, Cloud Run, GKE (Google Kubernetes Engine), Cloud SQL, and Pub/Sub.
• Hybrid Cloud and Integration: Managing the architectural connection and optimization of existing AWS (ECS, S3, Lambda) and Azure (AI Search) services, while maintaining GCP as the primary environment.
• AI-Driven Data and Memory Management: Optimizing Redis not just for caching, but for AI agent memory management, state tracking, and vector-based fast data retrieval processes.
• CI/CD and MLOps: Managing and optimizing end-to-end CI/CD pipelines within the GCP ecosystem; integrating software delivery with model evaluation workflows.
• Modern Database Architecture: Designing and managing relational databases alongside Vector Databases essential for LLM projects; utilizing ORM tools like SQLAlchemy at an advanced level.
• Advanced Engineering Principles: Applying 5-8 years of engineering foundation to the system through a deep understanding of data structures, algorithms, and design patterns. Adopting KISS (Keep It Simple, Stupid) and DRY (Don't Repeat Yourself) principles to keep complex AI workflows modular and maintainable.
• Quality and Test Automation: Using Pytest to ensure software quality through unit tests, and building advanced frameworks to evaluate the accuracy of AI agent functions and outputs.
• Containerization and Versioning: Utilizing Docker for containerization and scalability, and managing all development lifecycles strictly with Git.