Build Large IT Projects at Enterprise Scale to Stay Relevant and Marketable
I get pinged by developers who either need a more meaningful IT job, or just got laid off from one. My advice to them is to start building large IT projects that you can claim ownership or authorship.
In a rapidly evolving tech landscape, staying relevant and marketable is a constant challenge for developers. Whether you’re seeking a more meaningful IT role or have recently faced a layoff, the path to securing your career lies in taking ownership of large-scale IT projects. Focusing on projects that demonstrate your authorship and expertise can open doors to exciting opportunities and lucrative positions.
A thriving career in IT begins with the realization that ownership matters. By directing your efforts toward projects where you can claim authorship or ownership, you set yourself apart in a competitive job market. Developers who attain a compensation of at least $200K USD do so by actively seeking higher visibility projects where they can place their name on it. Becoming the main driver or system designer/architect became the catalyst for their success.
But size matters when it comes to IT projects. The larger the scope and impact of your work, the higher the demand you can generate for your expertise. Rather than chasing certifications, build end-to-end IT projects, focusing on the project’s scale, technology stack, and the challenges it addresses. By choosing projects that align with your skills and aspirations, you’re better positioned to command attention from companies outside the FAANG realm. The financial rewards are out there for those who are the owner/primary driver for large enterprise projects.
But staying relevant requires more than just surface-level knowledge. For instance, understanding Kubernetes is valuable, but the real distinction comes from knowing how to deploy complex systems like Large Language Models (LLMs) at an enterprise scale. Imagine implementing an LLM solution that tackles pressing problems and ensures high transactional volume with optimal security measures in place. By incorporating tools like Vault and Istio, you not only enhance the technology but also position yourself as a sought-after consultant, capable of earning $500/hour.
As the importance of data security grows, so does the demand for proprietary solutions. I will continue to use the example of building a secure, production LLM implementation. Many companies are apprehensive about their sensitive data leaking to public LLMs like ChatGPT. This presents a golden opportunity for developers to create on-premise versions of LLMs, complete with advanced features and the ability for their engineering teams to manage API access, all behind the company firewall. Proving the viability of such a solution at scale not only garners attention but also provides a gateway to new opportunities.
If you want to ride the current wave for IT, then MLOps, particularly LLMOps, is an in-demand field with promising prospects. Upskilling as an ML Engineer and focusing on deploying AI/ML models at enterprise scale is a savvy move. When you build this project end-to-end, you focus on manually moving the model between development, testing, and production environments. As you master this skills, then you’re ready to automate and scale it with MLOps as an MLOps engineer. Developing end-to-end projects that span the ML development lifecycle, from development to production, while emphasizing Continuous Monitoring and Continuous Training, sets you apart as an MLOps engineer. This specialized skillset differentiates you from traditional DevOps roles, translating to higher earning potential.
Despite being the one to drive the large IT project, forming strategic partnerships is still key to tackling complex projects. For the LLM project example used in this article, teaming up with an MLOps engineer (or LLMOps engineer if you can find one!) and senior data scientist skilled in Natural Language Processing (NLP) creates a powerful consultancy unit. By honing your collective expertise, you can address the specific needs of large enterprises, offering secure, accurate, and proprietary LLM solutions that stay behind the corporate firewall.
In conclusion, building large IT projects at enterprise scale is more than a career move; it’s a journey toward sustained relevance and marketability. Embrace ownership, tackle impactful challenges, and continually upskill to stay at the forefront of the tech industry. By mastering technologies, addressing security concerns, and collaborating effectively, you position yourself to thrive in an ever-changing landscape. If you’re serious about this transformative path, take the first step and contact me, to seize the opportunities that await.
If you're looking for support from me, here are a few options:
Enterprise Data Science Consultancy: With my consult team comprised of a Senior Data Scientist, Senior ML Engineer, Senior Data Engineer, and Senior Cloud Engineer, we will help you architect and build your Enterprise Data Science platform, and transfer knowledge to your IT team to maintain and optimize it. We will also overlay an MLOps framework to manage the AI solutions you build on this platform. If you don’t have an MLOps team, we will help you build one. Please get in touch about this consultancy here
Coaching and Mentorship: I offer coaching and mentorship; book a coaching session here