Generative AI Software Engineer

Responsibilities

- Collaborate with cross-functional teams to understand business needs and translate them into backend functionalities for GenAI and Agentic AI projects.

- Design, develop, and maintain scalable backend solutions, including event-driven architectures and integration with external systems/APIs.

- Manage data storage solutions using relational (PostgreSQL, MySQL) and NoSQL (MongoDB, DynamoDB) databases to support AI applications in production.

- Utilize containerization (Kubernetes) and implement DevOps practices, including CI/CD pipelines (Azure DevOps, GitHub Actions), for efficient deployment and scalability.

- Build and integrate APIs using Python frameworks (Flask, FastAPI) and collaborate with data scientists, engineers, and DevOps teams for seamless AI model deployment.

What You Must Have

- At least a Bachelor's & Master's degree
- At least 4 years of experience
- Oral and written proficiency in English required

What Sets You Apart

- Proficient with LLM interaction frameworks like LangChain, Semantic Kernel, and LlamaIndex, and experienced in integrating, scaling, and deploying GenAI and agentic applications in production.

- Skilled in setting up data pipelines for both model training and real-time inference to support AI workloads efficiently.

- Advanced Python expertise including OOP, asynchronous programming (asyncio), concurrency (multithreading, multiprocessing), design patterns, memory management, and performance optimization for scalable GenAI systems.

- Strong foundation in data structures, algorithms, software design principles (SOLID, clean architecture), and hands-on experience with cloud-native development on Azure/AWS, including serverless, microservices, and container orchestration (Kubernetes, Docker).

- Experience with additional OOP languages (Java, C++, C#) and familiarity with WebSocket implementations for real-time application functionality.