Full Stack Engineer

Key Responsibilities:

• Design, develop and maintain production-grade software systems that power client's AI and data science solutions. This includes developing web applications, APIs, backend services, frontend, and platforms that enable the deployment and adoption of AI products across the agency.
• Build scalable AI applications by integrating large language models (LLMs), retrieval systems, vector databases, agent frameworks and other modern AI technologies into robust software solutions. You will work closely with data scientists to operationalize machine learning and generative AI capabilities.
• Engineer secure, reliable and maintainable software using modern development practices, including CI/CD, automated testing, containerization, infrastructure-as-code and cloud-native architectures.
• Contribute to the evolution of client's AI engineering capabilities by developing reusable frameworks, internal libraries, developer tools and engineering best practices that accelerate AI solution delivery across the agency.
• Monitor, troubleshoot and continuously improve deployed AI systems to ensure performance, scalability, security and operational reliability.
• Keep abreast of emerging software engineering and AI technologies, evaluating new tools and frameworks that can improve client's AI development capabilities.

Pre-Requisites for the Role:

• Degree in Computer Science, Software Engineering, Information Systems or a related discipline.
• Strong software engineering fundamentals, including object-oriented programming, software architecture, design patterns and clean coding practices.
• Strong proficiency in Python and experience with modern backend frameworks (e.g. FastAPI, Flask, Django, React).
• Experience building full-stack applications, including RESTful APIs, authentication, databases and frontend integration.
• Experience developing cloud-native applications using platforms such as AWS, Azure or Google Cloud Platform.
• Familiarity with containerization and deployment technologies such as Docker, Kubernetes and CI/CD pipelines.
• Familiarity with AI engineering concepts such as LLMs, Retrieval-Augmented Generation (RAG), embeddings, vector databases, agentic workflows and AI orchestration frameworks (e.g. LangChain, LangGraph or similar).
• Strong analytical and problem-solving skills with the ability to translate complex business requirements into scalable software solutions.
• Excellent communication skills and ability to work effectively within multidisciplinary teams comprising software engineers, data scientists, business stakeholders and IT partners