AI Engineer
Job Summary
This project focuses on building and evolving an Agentic SDLC framework designed to support the development of modern AI-driven systems. The framework incorporates regulatory considerations and practical lessons from scaling agentic solutions across different environments. You will be responsible for designing, building, and maintaining scalable AI applications and intelligent system architectures.
Responsibilities
- Design and build scalable AI-driven applications
- Develop and integrate intelligent features
- Maintain system reliability and performance
- Implement secure microservice architectures
- Deploy solutions using CI/CD pipelines
Required Skills
- Python (FastAPI, Pydantic, pytest)
- Generative AI, LLMs, and RAG
- LangGraph and Vector databases
- Microservice architecture and Docker
- Azure Cloud and Azure DevOps
Job Details
### Responsibilities
- Design, build and maintain scalable AI-driven applications and services.
- Develop and integrate intelligent features into existing systems.
- Ensure reliability, performance and quality of solutions using strong engineering practices and testing.
- Implement secure and compliant application flows.
- Design scalable system and microservice architectures.
- Work with data and AI components for information retrieval and processing.
- Develop, deploy and operate solutions in cloud environments using automated CI/CD pipelines.
- Monitor system performance and ensure production readiness.
- Collaborate with cross-functional teams to design, build and deliver end-to-end solutions.
- Participate in technical discussions, architecture decisions and solution design reviews.
- Share knowledge and promote engineering best practices across teams.
### Requirements
- Strong Python development skills (Python 3.11+, including FastAPI and Pydantic).
- Hands-on experience with testing Python applications such as pytest and testcontainers.
- Solid understanding of application security principles, including OAuth and OIDC.
- Experience designing and building microservice architectures.
- Practical experience with Generative AI (LLMs, Agentic workflows, RAG, LangGraph, Vector and graph databases).
- Experience working with containerization technologies such as Docker.
- Cloud experience, preferably with Microsoft Azure.
- Experience with CI/CD pipelines such as Azure DevOps.
- Experience with Databricks (optional).