As a senior platform and AI engineering leader, you will architect, build, and evolve complex distributed systems that power intelligent applications at scale. You will work with cutting-edge AI frameworks and cloud-native infrastructure to deliver resilient, high-performance platforms that support modern AI workloads, including retrieval-augmented generation (RAG) and large language model (LLM)–driven applications.
This role goes beyond hands-on development—you will influence technical direction, mentor senior engineers, and partner closely with cross-functional stakeholders to ensure architectural decisions align with business and product goals. You will set engineering standards, guide platform evolution, and ensure systems are built with reliability, security, and observability at their core.
Key Responsibilities
Lead the architecture and design of large-scale distributed systems and AI platforms
Build and optimize AI-driven systems using LLM orchestration frameworks, vector databases, embeddings, and RAG patterns
Drive platform decisions involving LangChain, LlamaIndex, and modern AI orchestration stacks
Design cloud-native infrastructure using AWS or Azure with Kubernetes-based deployments
Establish scalable event-driven and streaming architectures using messaging platforms such as Kafka
Implement infrastructure-as-code and automation using tools like Terraform
Define and enforce best practices for reliability, performance, security, and observability
Oversee system monitoring, logging, tracing, and operational readiness at scale
Provide technical leadership across teams, influencing architecture, roadmap planning, and platform standards
Mentor senior engineers and foster a culture of engineering excellence and innovation
Partner with product, data, and leadership teams to translate complex requirements into robust technical solutions
Evaluate emerging technologies and frameworks to continuously improve platform capabilities
What Makes You a Great Fit
10–15+ years of experience in distributed systems, platform engineering, or large-scale backend architecture
Deep expertise in AI and machine learning platforms, including LLM orchestration, vector databases, embeddings, and RAG workflows
Strong hands-on experience with LangChain, LlamaIndex, and modern AI tooling ecosystems
Proven experience building cloud-native systems using AWS or Azure, Kubernetes, and containerized environments
Solid background in streaming systems, event-driven architecture, and platform scalability
Experience implementing infrastructure automation, CI/CD pipelines, and observability systems
Demonstrated leadership in driving architecture decisions for complex, high-impact systems
Strong communication skills with the ability to influence technical direction across senior stakeholders
A strategic mindset combined with hands-on technical depth and execution excellence
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