Description

You will architect secure, scalable, and observable distributed systems, integrating LLMs and GenAI capabilities into enterprise platforms. You define service architecture strategies, API contracts, and runtime patterns across multiple teams while mentoring engineering groups.

Responsibilities

• Design and govern enterprise-scale backend platforms that are highly available, performant, and cost-efficient.
• Architect inference systems including routing, batching, caching, and cost optimization for LLM integrations.
• Define service architecture standards, API contracts, and data contracts across development teams.
• Implement AI-augmented backend architectures with retrieval-augmented generation (RAG) and agent-based flows.
• Champion developer experience by improving CI/CD quality gates, test automation, and observability-first design.

Required Skills

• 12+ years of backend engineering experience with at least one major ecosystem: Node.js/TypeScript, Java/Kotlin, .NET, Python, or Go.
• Strong expertise in distributed systems, microservices, event-driven architectures, and consistency models.
• Hands-on experience with cloud platforms (AWS preferred; Azure/GCP acceptable) and infrastructure as code.
• Proficiency with data and streaming technologies: SQL, NoSQL, Kafka, and Redis.
• Experience integrating LLMs, prompt engineering, and AI quality metrics within enterprise applications.
• Deep understanding of system design fundamentals: caching, resilience patterns, fault tolerance, and security.
• Strong background in operational excellence, including observability, performance tuning, and incident response.

Preferred Skills

• Experience with API gateways, service mesh, and networking fundamentals.
• Knowledge of AI safety, governance, and responsible AI principles.

Key Skills

Education

Any Graduate