In 2025, I predict we’ll see a shift in traditional software development as the tech industry embraces AI-driven workflows. Below is an example job description for what I believe will become one of the most sought-after roles as AI continues to reshape the industry. Developers must learn these skills now—or risk being left behind.
We are seeking an innovative and strategic Augmented Developer and Integration Engineer (ADIE) to lead the integration of artificial intelligence into our enterprise systems, revolutionizing software development and business operations. This role focuses on building AI-powered solutions that enhance productivity, enable real-time decision-making, and provide seamless integration with existing enterprise systems. You will design and implement scalable AI architectures, facilitate Retrieval-Augmented Generation (RAG) capabilities, and build APIs that support agentic access to databases and business systems, ensuring an intelligent and interconnected ecosystem.
- AI Strategy and Integration: Develop and execute strategies to incorporate AI into workflows and systems, ensuring smooth integration with existing enterprise infrastructure.
- Systems Integration: Design and implement solutions to connect AI systems with databases, APIs, and business platforms, enabling RAG and agentic capabilities for real-time insights and automated decision-making.
- API Development and Maintenance: Build and maintain robust, scalable APIs that enable agentic access to enterprise databases and business systems, facilitating AI-driven automation and advanced queries.
- Prompt Engineering and Oversight: Craft, refine, and validate AI prompts to guide tools in generating accurate, context-aware results.
- AI-Assisted Development: Leverage AI tools to generate and refine code, focusing on system compatibility and the seamless integration of AI outputs.
- Code Validation and Adaptation: Review and debug AI-generated outputs, ensuring they meet technical requirements, security standards, and business goals.
- Architectural Design: Design scalable, efficient, and secure architectures for AI solutions, ensuring interoperability with existing systems and future scalability.
- Governance and Compliance: Ensure all AI systems adhere to data privacy regulations and ethical standards, mitigating risks related to security, bias, or compliance.
- Innovation and Research: Stay current with AI advancements, identifying opportunities to implement emerging technologies that provide a competitive edge.
- Education and Mentorship: Train team members on effective use of AI tools, emphasizing prompt engineering, API integration, and ethical AI practices.
- Bachelor’s or Master’s degree in Computer Science, Data Science, or AI Systems Engineering (not a real degree...yet), or related professional experience in software development, solutions architecture, data engineering, or AI integration.
- Proven expertise in system integration, including connecting AI solutions with databases, APIs, and enterprise business systems.
- Strong understanding of programming concepts (e.g., algorithms, data structures, object-oriented and functional programming).
- Proficiency with AI/ML tools (e.g., GitHub Copilot, ChatGPT, TensorFlow, PyTorch) and programming languages (e.g., Python, JavaScript) with the ability to validate, optimize, and adapt AI-generated code.
- Expertise in prompt engineering and fine-tuning large language models (LLMs).
- Familiarity with cloud-based AI services (e.g., AWS SageMaker, Azure AI, Google AI).
- Strong understanding of API design and maintenance, emphasizing scalability, security, and real-time performance.
- Certification in AI or cloud computing platforms (e.g., AWS Certified Machine Learning, Google Professional Machine Learning Engineer).
- Experience with RAG architectures and agentic capabilities in AI applications.
- Experience in building and managing APIs that support AI-driven automation.
- Understanding of AI ethics, bias mitigation, and responsible AI practices.
