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Principal Engineer
Hiver
Bengaluru, IND
Full-time
Senior · 12+ years experience
Hybrid
Discovered 2 weeks ago
AI frameworksfoundation modelsembeddingsvector databasesLLMs
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Key Responsibilities
- Set technical direction for both products. Make the architecture calls others will live with for years.
- Set technical standards across services, platforms, and engineering teams. Raise the bar on system design, code quality, AI integration, and engineering velocity.
- Conceptualize and build internal AI platforms, eval management, agentic dev tooling, codegen pipelines, AI-assisted QA, retrieval and inference infrastructure. Change the unit economics of building software at Hiver.
- Operate at the bleeding edge of AI-native product engineering. Stay ahead of what's possible. Bring it back in code and in how we build.
- Prevent the predictable failure modes of a scaling product org: unmaintainable systems, reliability decay, manual-QA bloat, slow integration of AI primitives into the product.
- Stay hands-on. The people best positioned to challenge how we build are the ones still building.
Key Requirements
- 12–14 years of engineering experience, anchored in a strong IC foundation. Most of your time today goes into architecture, technical direction, and platform building but you still code when it sharpens a decision or proves a hypothesis.
- Cloud and platform expertise . 10+ years building scalable Cloud/SaaS products, enterprise infrastructure software, or developer platforms.
- Technical depth in AI. Deep, current understanding of the AI landscape. Hands-on fluency with AI frameworks, foundation models, embeddings, and vector databases. You have shipped AI features, built AI platforms, and have opinions on what works and what does not in the real world.
- Proven ability to influence engineering leaders, product teams, and stakeholders to achieve outcomes in complex environments.
- Acts as a force multiplier leveraging technical credibility to drive alignment and to overcome organizational barriers & friction points.
- Demonstrated ownership of large-scale architecture decisions in production. SaaS environment preferred.
- Fluency in both halves of the modern stack: classical distributed systems, and the new stack of LLMs, evals, agentic systems, retrieval, and inference economics.
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