Job Title: Generative AI/ AI Full Stack Engineer

Overview

Are you passionate about leveraging AI to drive transformative changes within an organization? Do you enjoy working collaboratively with cross-functional teams to infuse AI capabilities into various business functions?

Automation & AI Center of Excellence (CoE), stands at the forefront of innovation, leveraging the latest advancements in Automation & Generative AI to drive internal transformation. Join us in the pursuit of not just solving business challenges across Allianz Technology’s Infrastructure, but reshaping the landscape for our employees through creative AI solutions. Immersing ourselves in the technology we deliver to our clients, we're not just adapting to change; we're driving it.

About the Role
We are seeking a talented Generative AI/ AI Full Stack Engineer to design, develop, and deploy cutting-edge AI-powered applications. This role involves leveraging state-of-the-art generative AI and classical AI models to create innovative solutions while ensuring a seamless full-stack implementation. You will work on integrating AI capabilities into scalable, user-friendly applications that solve complex problems and drive business transformation.


What You Bring

Degree in Computer Science or related field
• 4 to 7 years of working experience as a software developer in AI-focused cloud/container application development.
• A solid foundation in computer science, with strong competencies in algorithms, data structures, objects-oriented programming, design patterns, multi-threaded programming, and software design principles
• Sound experience in cloud native development
• Strong coding development and debugging skills, including hands-on experience with Java, JavaScript, GoLang and Python.
• Hands-on experience in developing REST APIs using frameworks such as Flask, FastAPI etc.

• Strong background with Java or JavaScript/TypeScript development building scalable APIs using Spring Boot, Node.js, Knowledge of Angular/React/Vue.js

• Strong background in machine learning and deep learning algorithms. - Proficiency in Python, TensorFlow, and PyTorch, Huggingface for developing AI models.

Generative AI frameworks – Langchain, LlamaIndex,

Agentic Frameworks - AutoGen, Semantic kernel, crewAI, promptflow,Langflow, Langraph

• Deep understanding of transformer architectures and diffusion models

• Experience in generative AI techniques such as GANs and VAEs. - Ability to design and implement scalable and efficient AI systems.
• Experience working with DevOps including but not limited to container technologies like Docker & Kubernetes, as well as Cloud Native technology stack such as Argo, Helm, etcd, and Envoy

• Hands-on experience with frontend frameworks (React, Angular, Vue) and backend technologies (Node.js, Django, Flask).

• Cloud platforms (AWS, Azure) and containerization tools (Docker, Kubernetes).

• Strong understanding of database technologies (SQL, NoSQL, vector databases,Graph databases).
• Knowledge of prompt engineering, fine-tuning, and embeddings for AI applications.
• Familiarity with MLOps tools like MLflow, Kubeflow, or SageMaker.
• Prior experience deploying AI-driven applications in production environments.
• Experience working with message queues such as Kafka and a clear understanding of pub-sub systems is bonus

Key Responsibilities

Generative AI Development
- Research, fine-tune, and deploy generative AI & LLM models for specific use cases.
- Design and implement solutions for text generation, image synthesis, conversational AI, and more.
- Optimize AI models for performance, scalability, and real-time applications.

Backend Development
- Build robust APIs and microservices to integrate AI models into web and mobile applications.
- Develop efficient data pipelines for model training and inference.

Work with databases (SQL/NoSQL) Vector DB/Graph DB to handle structured and unstructured data
- Ensure backend systems are secure, scalable, and capable of handling AI workloads.

Frontend Development
- Create intuitive user interfaces for applications powered by generative AI/ AI
- Collaborate with designers to bring AI-driven user experiences to life using frameworks like React, Angular, or Vue.
- Implement dynamic visualizations or outputs generated by AI models.

Cloud and Deployment
- Deploy AI models and full-stack applications on cloud platforms (AWS, Azure).
- Implement MLOps practices for model lifecycle management, monitoring, and retraining.
- Use containerization and orchestration tools (Docker, Kubernetes) to ensure scalability and reliability.