Required Skills & Experience
- 0 to 1+ years of experience in software engineering, backend development, AI engineering, or a related role.
- Hands-on experience with AWS cloud services, especially services used in modern AI application development.
- Good understanding of LLM-based applications, prompt engineering, and chatbot design.
- Working knowledge of Node.js or TypeScript is a plus.
- Experience integrating with REST APIs, JSON payloads, authentication flows, and databases.
- Familiarity with Git, CI/CD, and software development best practices.
Nice to Have Skills & Experience
- Experience with Amazon Bedrock for chatbot or GenAI use cases.
- Experience with LangChain, LangGraph, CrewAI, or similar orchestration frameworks.
- Exposure to multi-agent or agentic patterns.
- Experience with vector databases or search platforms such as OpenSearch, Pinecone, FAISS, or similar.
- Familiarity with serverless design patterns.
- Exposure to cloud security, IAM, and monitoring fundamentals.
- Experience automating business processes or internal workflows.
Job Description
We are seeking an AI Engineer with 0-2 years of experience to support the development of AI-powered applications, enterprise chatbots, and automation solutions within AWS. This role will focus on Retrieval-Augmented Generation (RAG), agent-based workflows, LLM integrations, and business process automation. The ideal candidate has a strong foundation in software engineering, cloud technologies, and modern AI application development.
Responsibilities
- Design, develop, and maintain AI applications using AWS services such as Amazon Bedrock, Lambda, API Gateway, S3, DynamoDB, CloudWatch, and Step Functions.
- Build enterprise chatbots and assistants using RAG over internal documents, knowledge bases, and business data.
- Develop prompt workflows, tool-calling patterns, and simple agentic flows for task execution.
- Integrate AI applications with internal systems, databases, REST APIs, and third-party platforms.
- Support ingestion, chunking, embedding, retrieval, and grounding pipelines for knowledgebased assistants.
- Build backend services and orchestration logic using Python and/or Node.js.
- Implement automation workflows for repetitive operational tasks, using AI where it adds value and standard automation where it is more appropriate.
- Participate in prompt tuning, response evaluation, testing, debugging, and performance improvement.
- Implement logging, retries, error handling, and monitoring for deployed solutions.
- Collaborate with business stakeholders, architects, and developers to convert use cases into working AI solutions.