Asks for

The ad says“Hands-on experience with AI orchestration frameworks (AI SDK, LangChain, LlamaIndex, or equivalent)”
The ad says“Hands-on experience with AI orchestration frameworks (AI SDK, LangChain, LlamaIndex, or equivalent)”
The ad says“Architected scalable backend systems including async pipelines, queues, caching, and db design”
The ad says“Core AI fundamentals: quantization, attention, tokenization, inference economics, fine-tuning vs prompting trade-offs”
The ad says“Architected scalable backend systems including async pipelines, queues, caching, and db design”
The ad says“Solid grasp of SQL, vector databases, RAG pipelines, embeddings, chunking strategies, and retrieval evals”
The ad says“Architected scalable backend systems including async pipelines, queues, caching, and db design”
The ad says“Solid grasp of SQL, vector databases, RAG pipelines, embeddings, chunking strategies, and retrieval evals”
The ad says“Core AI fundamentals: quantization, attention, tokenization, inference economics, fine-tuning vs prompting trade-offs”
The ad says“Core AI fundamentals: quantization, attention, tokenization, inference economics, fine-tuning vs prompting trade-offs”
The ad says“Strong JavaScript/TypeScript chops; And system design experience”
The ad says“Hands-on experience with AI orchestration frameworks (AI SDK, LangChain, LlamaIndex, or equivalent)”
The ad says“Hands-on experience with AI orchestration frameworks (AI SDK, LangChain, LlamaIndex, or equivalent)”
The ad says“Core AI fundamentals: quantization, attention, tokenization, inference economics, fine-tuning vs prompting trade-offs”
The ad says“Core AI fundamentals: quantization, attention, tokenization, inference economics, fine-tuning vs prompting trade-offs”
The ad says“Architected scalable backend systems including async pipelines, queues, caching, and db design”
The ad says“Solid grasp of SQL, vector databases, RAG pipelines, embeddings, chunking strategies, and retrieval evals”
The ad says“Solid grasp of SQL, vector databases, RAG pipelines, embeddings, chunking strategies, and retrieval evals”
The ad says“Solid grasp of SQL, vector databases, RAG pipelines, embeddings, chunking strategies, and retrieval evals”
The ad says“Strong JavaScript/TypeScript chops; And system design experience”
The ad says“Core AI fundamentals: quantization, attention, tokenization, inference economics, fine-tuning vs prompting trade-offs”
The ad says“Strong JavaScript/TypeScript chops; And system design experience”
The ad says“Solid grasp of SQL, vector databases, RAG pipelines, embeddings, chunking strategies, and retrieval evals”

Senior AI Engineer

Libra AI · Bengaluru (HSR Layout) · Full-time, on-site

We're building an agentic work OS with semantic search, 250+ integrations, and real workflows running in production. Looking for an engineer who can ship across the stack, set technical direction, and actually understands what's happening under the LLM hood.

The role

This is a hybrid IC + pod lead role. You'll be heads-down writing code on the hardest parts of the stack while also shaping how the rest of the engineering team thinks, builds, and ships. You'll architect and own the backend systems that power agentic workflows at scale (orchestration pipelines, retrieval layers, eval infra) and raise the bar for everyone around you.

What we're looking for

Must-haves

4 to 5+ years in a strong SDE role, shipping production systems (Strong backend experience)
Hands-on experience with AI orchestration frameworks (AI SDK, LangChain, LlamaIndex, or equivalent)
Solid grasp of SQL, vector databases, RAG pipelines, embeddings, chunking strategies, and retrieval evals
Core AI fundamentals: quantization, attention, tokenization, inference economics, fine-tuning vs prompting trade-offs
Strong JavaScript/TypeScript chops; And system design experience
Architected scalable backend systems including async pipelines, queues, caching, and db design
Has worked on systems that have seen real scale (traffic, data, or concurrency)
Strong communicator who can articulate technical trade-offs clearly and bring a team along
Demonstrated leadership: has mentored engineers, owned architecture decisions, or led a pod
Genuine passion for AI. You read papers on weekends, ship side projects, and your GitHub looks like a green forest. Bonus if those side projects are AI-flavored.

Bonus

DevOps fluency (Docker, K8s, CI/CD, infra-as-code)
Prior experience at an AI-first company or shipping LLM products
Exposure to graph databases, agent frameworks, or multi-model routing

What we offer

Top-of-market cash plus meaningful early-stage equity
A real seat at the table. You'll architect and lead, not just implement
Directly work with the founder and the core team on problems that actually matter
High-agency environment with the resources of a well-funded pre-seed company