Description

Key Responsibilities •             SDK Development: Design, build, and maintain client-facing AI SDKs in languages like TypeScript, Python, or Go to interact with backend LLM services and agent frameworks. •             Scaffolding & Boilerplate Tools: Create command-line interfaces (CLIs) and project starter templates that let engineers spin up production-ready AI applications in minutes. •             API & Interface Design: Establish clean, intuitive, and well-documented APIs for internal or external developers consuming AI capabilities. •             Agent & Workflow Integration: Embed support for modern LLM orchestrators (such as LangChain, LlamaIndex, or native model APIs) directly into the developer scaffolding. •             Testing & Reliability: Implement rigorous automated testing, validation mechanisms, and mocking tools for AI SDK components to prevent regressions. •             Documentation & Evangelism: Write comprehensive developer documentation, quickstart guides, and code examples to drive adoption. •             Design and build reusable SDKs, libraries, frameworks, templates, and reference implementations for AI development. •             Develop project scaffolding for common AI solution patterns. •             Create reusable components for model invocation, prompt management, RAG, agent orchestration, logging, security, and evaluation. •             Build standardized interfaces that allow applications to work with multiple AI models/providers where appropriate. •             Develop reusable patterns for token management, caching, model routing, retries, error handling, and observability. •             Create developer tooling and automation to accelerate AI application delivery. •             Embed enterprise security and governance requirements into reusable frameworks. •             Establish coding standards, testing frameworks, package-management approaches, and version-management practices. •             Develop documentation, examples, starter applications, and engineering guidance. •             Partner with AI architects and platform engineers to create an integrated AI engineering harness. •             Support development teams adopting the SDK and gather feedback to continuously improve the framework. Required Skills •             Strong software engineering experience with Python and/or modern enterprise programming languages. •             Deep understanding of software framework and SDK design. •             Experience with APIs, microservices, reusable libraries, automated testing, CI/CD, and developer tooling. •             Hands-on GenAI engineering experience. •             Familiarity with LLM APIs, RAG, agents, embeddings, model orchestration, and evaluation frameworks. •             Strong understanding of software-design principles and enterprise engineering standards. Preferred Experience •             8+ years of software engineering experience. •             Experience developing internal developer platforms, SDKs, frameworks, or reusable application foundations. •             Demonstrated experience converting AI prototypes into standardized, production-ready capabilities

Key Skills

Education

Any Gradute