Engineers + AI: Why We Still Matter (and How to Future-Proof Your Career)

[072arushi]

Talk title

Engineers + AI: Why We Still Matter (and How to Future-Proof Your Career)

Short talk description

With AI tools writing code, generating designs, and automating daily tasks, it’s natural to question where engineers fit in. This talk explores how the role of engineers is evolving—not disappearing—in the age of AI. While AI delivers speed and scale, it also introduces risks like insecure systems, unreliable outputs, and biased decisions. Engineers remain essential to design safe systems, validate AI outputs, and take responsibility for security, ethics, and long-term impact in an AI-driven world.

Long talk description

With AI tools now writing code, generating designs, and automating everyday engineering tasks, it’s natural to wonder where engineers fit in anymore. Are we slowly being replaced — or is our role simply changing?

This talk takes a grounded look at what engineering really means in the age of AI. While AI is incredibly powerful at producing fast results, it also brings new risks: insecure systems, unreliable outputs, data leaks, and models that fail without warning. AI can sound confident and still be wrong, biased, or vulnerable to misuse. Managing these risks is not something AI can do on its own.

Engineers are the ones who design systems around AI, decide where it should be used (and where it shouldn’t), and take responsibility when things go wrong. From validating outputs to thinking through security, ethics, and long-term impact, engineering judgment matters more than ever.

This session is for students and professionals who want to move beyond the hype, understand the real challenges of AI-driven systems, and learn how to stay relevant, trusted, and impactful in an increasingly automated world.

What format do you have in mind?

Talk (20-25 minutes + Q&A)

Talk outline / Agenda

1. Introduction: The AI Anxiety
   Setting the context- with AI tools writing code and automating workflows, are engineers being replaced, or is the role evolving?

2. What AI Can and Cannot Do
   Exploring AI’s strengths (speed, scale, pattern recognition) versus its limitations (context, judgment, accountability).

3. AI Security & Trust Challenges
   Understanding new risks introduced by AI systems, including data leakage, prompt injection, unreliable outputs, and model vulnerabilities, and why engineers must design secure, trustworthy systems.

4. The Evolving Role of Engineers
   From writing code to owning systems: validation, human-in-the-loop design, ethical considerations, monitoring, and long-term accountability.

5. Future-Ready Skills & Practical Roadmap
   Key skills to build- AI literacy, security mindset, systems thinking, and communication, along with actionable steps for students and professionals.

Key takeaways

1. A realistic understanding of how AI is transforming engineering roles, and why engineers are becoming more essential, not obsolete.
2. Insight into the limitations and security risks of AI systems, including unreliable outputs, data exposure, and misuse.
3. An understanding of AI security as a core engineering responsibility, not an optional add-on.
4. Clarity on the evolving role of engineers as system designers, validators, and owners of trust and accountability.
5. A practical roadmap of future-ready skills- including AI literacy, systems thinking, security mindset, and communication to stay relevant and impactful in an AI-driven world.
6. Actionable next steps that students and professionals can apply immediately in their projects or workplaces.

What domain would you say your talk falls under?

Careers and Community

Duration (including Q&A)

30 minutes (25 mins talk + 5 mins Q/A) + can take up doubts offline

Prerequisites and preparation

NA

Resources and references

NA

Link to slides/demos (if available)

No response

Twitter/X handle (optional)

No response

LinkedIn profile (optional)

https://www.linkedin.com/in/arushi-garg105

Profile picture URL (optional)

No response

Speaker bio

MTS-2 at Adobe
AI Enthusiast
GSSoC’24 Program Manager
AWS Cloud Captain’23
Mentor | Speaker | Researcher
23k+ Community on LinkedIn

Availability

Saturday, 28 Feb 2026

Accessibility & special requirements

NA

Speaker checklist

• I have read and understood the PyDelhi guidelines for submitting proposals and giving talks
• I will make my talk accessible to all attendees and will proactively ask for any accommodations or special requirements I might need
• I agree to share slides, code snippets, and other materials used during the talk with the community
• I will follow PyDelhi's Code of Conduct and maintain a welcoming, inclusive environment throughout my participation
• I understand that PyDelhi meetups are community-centric events focused on learning, knowledge sharing, and networking, and I will respect this ethos by not using this platform for self-promotion or hiring pitches during my presentation, unless explicitly invited to do so by means of a sponsorship or similar arrangement
• If the talk is recorded by the PyDelhi team, I grant permission to release the video on PyDelhi's YouTube channel under the CC-BY-4.0 license, or a different license of my choosing if I am specifying it in my proposal or with the materials I share

Additional comments

No response

[kybrdbnd]

hi @072arushi

Thank you again for agreeing to present — we’re excited to have your talk as part of the lineup!

We noticed that the slides haven’t been shared yet. Could you please upload or share them at your earliest convenience? We’d like to review them and ensure everything is set up smoothly ahead of the session.

If you need any support or have questions, feel free to let us know.

[072arushi]

Hi @kybrdbnd
Thanks for accepting my proposal.
I need some time to work on my slides and will take a day or two.
Can I share the slides by the weekend?

[kirtivr]

Yes, please share the slides as soon as possible. In the first round, the slides need not be perfect, what really matters is that we understand the outline of your talk and the key insights it seeks to deliver.

Then after a few rounds of iteration, we can converge to the best possible talk.

[kirtivr]

The premise of your talk is interesting.

If I was preparing this talk I would pick the best arguments from both sides -

"Developers are going to be replaced, RLVR based reinforcement learning techniques mean that a lot of common tasks will be easily automated"

"Actually writing the code is only 30% of development. 70% of it has always been figuring out what problem to solve, and how to solve it satisfying all stakeholder constraints"

And not pick a side necessarily.

[072arushi]

Sure @kirtivr
I'll share the slides by tomorrow EOD

[072arushi]

Hi
These are my slides- https://www.canva.com/design/DAHBL7Xj0_M/MBiqznizoccFy-kxdNmmYA/edit?utm_content=DAHBL7Xj0_M&utm_campaign=designshare&utm_medium=link2&utm_source=sharebutton

The presentation is in a very initial draft phase. I'll be updating it, adding some more points, visualisations, memes, etc. But if we talk about the content, this is mainly what I am planning to cover in the session.

[vaibhavjain-in]

Hi @072arushi ,

Thank you for putting this deck together. It’s a timely topic with a strong foundation. To ensure this resonates with the audience, I’ve outlined a few areas for refinement:

1. Elevate the Narrative: In Slide 4, describing AI as "just a better keyboard" may undersell the technology. I suggest framing it as a "probabilistic reasoning engine." This shift in verbiage acknowledges the technical complexity and sets a more professional tone for the discussion.

2. Align Content with the "Architect" Title: While the title mentions the shift to System Architect, the current content focuses heavily on coding tasks and boilerplates. To bridge this gap, let’s explore how AI allows engineers to offload low-level implementation so they can focus on high-level design and system integrity.

3. Incorporate Architectural Fundamentals: A true architectural perspective should include "The Big Three": Observability, Cost, and Latency. Adding these dimensions will show the audience how AI impacts the entire system lifecycle, not just the IDE.

4. Balance Technical Depth: Slide 6 is a highlight—I’d love to see you lean into those points. Conversely, Slide 12 (Guardrails) feels a bit light. I recommend expanding this section with a visual diagram or workflow. Showing how "Safety Wrappers" or "Guardrails" actually function will provide the technical "meat" the audience is looking for.

5. Address the Business Impact: Every engineering decision eventually hits the bottom line. Try to answer the "So what?" for the business. How does this shift affect delivery velocity, long-term technical debt, or resource allocation? Connecting engineering roles to business outcomes is key for this level of talk.

6. Provide a Clear "Actionable Roadmap": People love frameworks they can use immediately. I suggest adding a structured "Path to Change" template or a step-by-step roadmap. Change management is difficult, and giving the audience small, achievable milestones makes the transition feel possible.

Overall, the core message is strong, but it needs a more systematic approach to the "Engineer-to-Architect" evolution.

Looking forward to the next iteration. Remember: Kaizen.

[072arushi]

Thanks @vaibhavjain-in, for the review
I'll definitely incorporate the suggestions in the ppt