The Art of Technical Leadership in the Deep Tech – Insights from Accomplished Technical Leader, Srikanta Datta Prasad Tumkur

Leadership in the software engineering technical track is evolving. It’s an impactful discipline driving multi-year roadmaps, collaborating with diverse stakeholders, and creating large-scale product lines that power enterprises. Srikanta Datta Prasad Tumkur, a Senior Staff Engineer of Director Level, oversees company wide projects and with over a decade of experience in Silicon Valley in Platforms, Cybersecurity, AI/ML systems in Series-B, Fortune-100/500 enterprises, and shares his journey and insights on how engineers can transition into high-impact technical leadership roles while leveraging platform engineering, cybersecurity and AI/ML systems. Mr Tumkur has earned a Master of

Science from Carnegie Mellon University and an Executive Masters Degree from HEC Paris Business School.

Srikanta, for many software engineers, the path to leadership seems synonymous with

managing people. How can engineers grow into technical leadership roles focused on scope and innovation rather than team management?

Srikanta: This is a common misconception—leadership doesn’t have to mean managing people. Technical leadership is about managing complexity, owning roadmaps, and driving innovation at scale.

To grow into such roles, focus on:

• Expand Your Impact: Start by taking ownership of fixing toil for the current team and foundationally address them, then expand to cross-team initiatives or projects that span multiple domains. For example, identifying, designing and delivering the redesign of a foundational system to improve performance by 30% demonstrates the ability to manage complex technical challenges.
• Master Communication: Influence comes from articulating technical ideas clearly to diverse audiences, including engineers, product managers, and executives. I’ve found frameworks like “Why-What-How” invaluable for presenting proposals.
• Show Long-Term Vision and gain Trust by execution: Leadership in deep tech is about thinking in terms of quarters and years, not in sprints or two weeks. For instance, developing and executing a year’s roadmap for transitioning to a hybrid architecture (including both

on-premise and multi-cloud) shows both technical depth and strategic foresight.

•             Leverage Mentorship: Seek out technical leaders who have followed this path. Early in my career, I benefited from mentors who helped me align my technical expertise with broader organizational goals.

Srikanta, technical leadership often requires owning roadmaps and collaborating across multiple stakeholders. How do you approach aligning long-term goals with day-to-day execution?

Srikanta: The key to aligning long-term goals with execution is to start with clarity of purpose and focus on “measuring what matters.” In the technical track, you don’t manage people directly, but you influence outcomes by ensuring alignment across teams and stakeholders.

Here’s how I approach it:

1. Start with the “Why”: A roadmap must answer why a feature or system matters to customers and the business. For instance, when working on a global platform for enterprise customers, I aligned the roadmap to measurable outcomes like reducing downtime by 30% and accelerating deployment cycles.
2. Break Down the Vision: Multi-year roadmaps can feel abstract. Breaking them into quarterly deliverables with tangible milestones ensures teams can see progress. I’ve used OKRs (Objectives and Key Results) extensively, tying each key result to engineering metrics like system latency, throughput, or defect rates.
3. Stay Flexible: A roadmap is a compass, not a map. It’s crucial to adapt as customer needs or technology landscapes evolve. For example, in one project, we pivoted mid-year to prioritize

integration with a new cloud provider, which ultimately added significant customer value.

You’ve mentioned “measure what matters.” How does this apply to technical leadership in large-scale projects?

Srikanta: “What gets measured gets improved.” But in technical leadership, the art lies in choosing the right metrics. Measuring too much creates noise, while too little leads to blind spots.

For large-scale projects, I focus on:

1. Customer-Focused Metrics: These include uptime, deployment success rates, or feature adoption rates. For a distributed platform I worked on, we prioritized reducing average downtime per customer, which became a key driver for engineering improvements.
2. Team Efficiency Metrics: Metrics like code deployment frequency or mean time to recovery (MTTR) show how efficiently teams are shipping and maintaining systems. When we

introduced automated observability tools, MTTR dropped from hours to minutes, and we tracked this as a success metric.

3. Business Value Metrics: Ultimately, technology serves business goals. Metrics like cost savings from cloud optimization or revenue impact from faster go-to-market cycles help validate roadmap priorities.

What’s your perspective on using technologies like AI, cybersecurity, and platform engineering to balance innovation and operational excellence?

Srikanta: These technologies are the pillars of modern deep tech systems. Together, they create a virtuous cycle of innovation and reliability:

1. Platforms for Velocity: A well-designed platform accelerates product delivery while ensuring governance. For example, I’ve seen developer portals reduce onboarding time for new features by 40%, allowing teams to focus on innovation.
2. Cybersecurity as an Enabler: Secure systems build customer trust. By embedding security into platforms (e.g., automated compliance checks in CI/CD), organizations can innovate faster without risking breaches.
3. AI for Observability: AI tools enhance system monitoring by predicting issues before they occur, reducing downtime and improving user experience.

These technologies are most impactful when aligned with clear business objectives.

Cybersecurity is a critical focus for deep tech systems. How should technical leaders approach building secure systems without compromising innovation?

Srikanta: Security must be embedded into the DNA of every system—what I call “security by design.” However, it shouldn’t stifle innovation; instead, it should enable it.

Here’s how:

1. Shift Left on Security: Integrate security checks early in the development process. For example, automated vulnerability scanning tools like Snyk can catch issues before deployment.
2. Adopt Zero Trust Architectures: In today’s distributed environments, zero trust principles—where every access request is verified—are critical. This approach ensures that even if one layer is compromised, the rest remains secure.
3. AI-Driven Defense: AI/ML is becoming indispensable for cybersecurity. For instance, anomaly detection systems can identify unusual behaviors in real time, enabling faster responses to potential threats. A great example is deploying AI models that predict breaches based on traffic patterns, allowing teams to act proactively rather than reactively.

AI and ML systems are often seen as deep tech game-changers. How can technical leaders ensure these systems deliver real business value?

Srikanta: AI/ML systems have enormous potential, but they’re only as valuable as the business problems they solve. The goal is to bridge technical innovation with measurable outcomes.

1. Start with the Problem: Define the business challenge first. For example, if customer churn is a problem, an ML model predicting churn likelihood is more valuable than a generic recommendation engine.
2. Simplify Deployment: Many organizations struggle with operationalizing AI. Tools like Kubeflow help manage ML pipelines, ensuring models move from experimentation to production seamlessly.
3. Measure Business Impact: Use KPIs to measure AI’s success. For instance, in an AI-driven fraud detection system, metrics like reduced false positives directly correlate with business outcomes.

The most successful leaders focus on practical AI applications that align with organizational goals rather than chasing trends.

How do you navigate the complexities of multi-year roadmaps when managing multiple stakeholders?

Srikanta: Multi-year roadmaps are a balancing act between delivering quick wins and laying the groundwork for long-term success. The key is “managing the present while building for the future.”

1. Anchor on Long-Term Vision: The roadmap must reflect the company’s north star. For example, when designing platforms that supported hybrid cloud environments, the long-term vision was to ensure seamless interoperability across providers.
2. Engage Stakeholders Regularly: Multi-stakeholder environments require constant communication. I’ve run bi-monthly technical steering meetings with architects, product managers, and security leads to ensure alignment and address roadblocks early.
3. Document and Share Milestones: A transparent roadmap helps stakeholders see how their priorities fit into the bigger picture. I’ve found tools like Notion or Confluence invaluable for keeping roadmaps updated and accessible.

What frameworks or principles do you use to make technical trade-offs?

Srikanta: Trade-offs are inherent in every technical decision, and the best outcomes come from applying timeless principles:

1. Optimize for the Critical Path: Focus on the part of the system that directly impacts customers. For example, in a platform serving thousands of users, we prioritized latency reduction over feature enhancements because it was mission-critical.
2. Adopt the “2-Way Door” Principle: Reversible decisions (like adopting a new CI/CD tool) should be made quickly, while irreversible ones (like choosing a database architecture) require extensive validation.
3. Leverage Pareto’s Principle: 80% of impact often comes from 20% of effort. Identifying and focusing on high-impact areas ensures resources are used efficiently.

What excites you most about technical leadership in deep tech?

Srikanta: What excites me most is how the role blends strategy and execution. As technical leaders, we don’t just build systems—we shape how technology impacts people, businesses, and industries.

One project I worked on involved building a platform for disconnected environments where enterprises couldn’t rely on real-time cloud access. Solving this challenge meant creating something both innovative and deeply practical. Seeing those systems adopted by thousands of users across

industries was immensely rewarding.

What drives me is the opportunity to bridge the cutting edge with the real world—making technology not just advanced, but meaningful.

Closing Thoughts

Srikanta’s insights showcase the art of leading and driving multi-year roadmaps, balancing stakeholder priorities, and building platforms that scale. For those navigating technical leadership in deep tech, his lessons are a masterclass in combining strategy, innovation, and operational excellence.