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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in designing and developing reusable components and APIs for AI adoption, with a strong focus on integrating Large Language Models and implementing serverless architectures within the AWS ecosystem. Proficient in establishing engineering best practices, technical documentation, and fostering cross-team collaboration.
Highest-signal resume keywords
Node.js DevelopmentAWS Services ExperienceGenerative AI IntegrationServerless Framework ProficiencyAPI Design and Development
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Node.jsTypeScriptJavaScriptAPI DevelopmentServerless ArchitecturesLarge Language ModelsObservabilityTesting and SecurityDistributed SystemsTechnical Documentation
Soft Skills
Strong CommunicationCross-Team CollaborationSelf-Driven MindsetHigh Degree of OwnershipTechnical Decision-Making
Tools & Technologies
AWS LambdaAPI GatewayDynamoDBAmazon BedrockServerless FrameworkEventBridgeSQSSNSModel Context ProtocolSemantic Search Technologies
Industry Keywords
Generative AIAI AdoptionSoftware Design PrinciplesEngineering Best PracticesEvaluation Frameworks
Tech Stack
Tools & technologiesAWSDistributed SystemsDynamoDBJavaScriptNode.jsTypeScript
About the role
Key responsibilities & impact- Design and develop reusable components, libraries, APIs, plugins, and tools that enable AI adoption across products and engineering teams
- Integrate AI capabilities into the internal development framework and make them easy for engineering teams to adopt
- Define reference architectures, technical patterns, standards, and best practices for Generative AI-based solutions
- Design and implement integrations with Large Language Models and AI services, primarily within the AWS ecosystem
- Develop production-ready solutions using Node.js, TypeScript, serverless architectures, and the Serverless Framework
- Explore and implement structured generation, tool calling, agents, RAG, MCP, embeddings, and semantic search
- Define testing and evaluation mechanisms for quality, security, latency, reliability, and cost of AI-powered solutions
- Implement observability for model usage, prompts, tokens, responses, errors, execution times, and other operational metrics
- Establish technical controls and engineering practices for secure and responsible AI usage
- Build proof-of-concepts and evolve successful approaches into scalable, maintainable, production-ready solutions
- Create technical documentation, implementation examples, and adoption guidelines
- Support development teams in adopting reusable AI components, architectures, and engineering best practices
- Participate in architecture reviews and contribute to technical decision-making
- Share knowledge and promote AI engineering best practices
Requirements
What you’ll need- Senior-level experience in backend software development
- Strong expertise in Node.js and solid experience with JavaScript and/or TypeScript
- Hands-on experience with AWS, serverless architectures, and distributed systems
- Experience designing and developing APIs, libraries, frameworks, plugins, or other reusable software components
- Strong understanding of software design principles and engineering best practices, including testing, security, observability, and CI/CD
- Experience contributing to software architecture, technical design, and engineering decision-making
- Hands-on experience developing or integrating solutions based on Large Language Models (LLMs) and Generative AI
- Ability to translate business and technical requirements into generic, scalable, and reusable solutions
- Ability to research emerging technologies, evaluate alternatives, and transform concepts and proof-of-concepts into production-ready solutions
- Self-driven mindset with a high degree of ownership and autonomy
- Strong communication, technical documentation, and cross-team collaboration skills
- Experience with Amazon Bedrock or other Generative AI platforms
- Experience with the Serverless Framework and plugin development
- Hands-on knowledge of RAG, AI agents, tool calling, Model Context Protocol (MCP), structured generation, embeddings, and semantic search
- Experience with AWS services such as Lambda, API Gateway, Step Functions, EventBridge, SQS, SNS, and DynamoDB
- Knowledge of vector databases and semantic search technologies
- Experience implementing evaluation frameworks, guardrails, observability, and operational practices for LLM-based applications
- Experience building internal development platforms, frameworks, or developer tools
Benefits
Comp & perks- Equal opportunities in recruitment, career development, and leadership
- Diverse and inclusive work environment