AI Backend Engineer

We are looking for a Senior Backend AI Engineer to build the core intelligence infrastructure powering next-generation AI-driven experiences. This role is focused on creating scalable, high-performance AI systems from the ground up — not simply integrating existing APIs.

The ideal candidate is a strong backend engineer with deep expertise in distributed systems, infrastructure, and scalable architecture, combined with practical experience building AI-native applications. This is a hands-on Individual Contributor role where technical ownership, architectural excellence, and high engineering velocity are critical.

What You Will Do

• Design, build, and scale backend systems that power AI-driven products and platforms.
• Architect highly reliable, low-latency, and high-throughput services from the ground up.
• Build AI-native infrastructure including LLM-powered workflows, RAG pipelines, orchestration systems, and model-serving architectures.
• Develop robust backend APIs, microservices, and distributed systems that operate at scale.
• Make critical architectural decisions around performance, scalability, reliability, and maintainability.
• Integrate modern AI technologies into production-grade software systems.
• Own projects end-to-end — from problem definition and architecture to implementation and deployment.
• Continuously improve engineering practices by leveraging modern AI development tools and automation.

Who We Are Looking For

Deep Backend & Infrastructure Expertise (8+ Years Experience)

You are a hardcore software engineer with strong backend fundamentals. You have experience building and scaling complex systems and understand:

• Distributed systems design
• Backend architecture and scalability challenges
• API design and service reliability
• Database performance and optimization
• High-throughput, low-latency systems

You have hands-on experience with technologies such as:

• Go / Rust / Java / Kotlin / Python
• Microservices architecture
• Kubernetes
• Kafka
• gRPC
• Cloud infrastructure and production systems

Strong Individual Contributor Mindset

• You are a hands-on engineer who writes production code regularly.
• You influence through technical excellence, architecture decisions, and engineering impact.
• You enjoy solving complex technical problems independently.
• You take ownership and drive outcomes without requiring heavy direction.

AI-Native Engineering Approach

You actively use modern AI tools to improve engineering productivity, including:

• Claude Code
• GitHub Copilot
• Cursor
• AI-assisted development workflows

You understand how AI can accelerate software development and use it to build, test, debug, and ship faster.

Practical AI Systems Experience

You have experience working with modern AI engineering ecosystems, including:

• LLM applications
• Retrieval-Augmented Generation (RAG)
• Vector databases (Pinecone, Weaviate, Milvus, pgvector, etc.)
• LLM orchestration frameworks
• Model serving and inference infrastructure
• AI workflow automation

You understand production AI systems beyond simple API integrations.

High Ownership & Startup Mindset

You thrive in ambiguous environments and:

• Take initiative without waiting for perfect requirements.
• Find solutions independently.
• Experiment, iterate, and ship quickly.
• Balance engineering quality with business impact.

Required Experience & Skills

• 8+ years of backend/software engineering experience.
• Strong experience designing and scaling production backend systems.
• Proven experience building systems from scratch.
• Strong understanding of distributed systems and infrastructure.
• Experience deploying and maintaining production applications.
• Practical exposure to AI/LLM-based systems.
• Strong programming skills in one or more backend languages.

Strong Resume Signals

Look for candidates who have:

• Architected platforms or core systems.
• Built backend infrastructure from scratch.
• Scaled systems from early-stage to high traffic.
• Owned major technical initiatives.
• Worked on platform, infrastructure, or AI engineering teams.

Preferred Background

Candidates from:

• AI-native startups building foundational AI products.
• Developer tools or AI-first SaaS companies.
• Core infrastructure/platform teams at engineering-driven organizations.

Red Flags

Candidates who are primarily:

• Pure ML researchers focused on model training, experimentation, or notebooks.
• Prompt engineers building basic chatbot wrappers without backend depth.
• Architecture-focused engineers who are no longer hands-on with code.
• Candidates focused more on processes and management rather than technical execution.

Ideal Candidate Profile

A builder who combines:

Deep backend engineering + scalable infrastructure expertise + practical AI systems knowledge + high ownership mindset.

Someone who can independently build the foundation for AI-native products and set a high engineering standard.