Entry - level AI Engineer responsible for developing, testing, and deploying ML and LLM - based solutions. Supporting foundational engineering skills across AI spectrum in a hybrid work environment.
About the role
- Designing advanced LLM solutions using Azure OpenAI for Kimberly-Clark. Collaborate with teams to build tech for care encompassing sustainability and inclusion.
Responsibilities
- Develop advanced LLM solutions using Azure OpenAI (GPT) with LangChain (prompt templates, chains, memory management)
- Build RAG pipelines covering data ingestion, chunking, embedding creation, and indexing (Azure Cognitive Search/FAISS), enabling grounded generation
- Create microservices and APIs in Node.js/Python, including REST and GraphQL endpoints (Express)
- Implement agent-based workflows and multi-agent orchestration, emphasizing robust error handling
- Construct ETL processes utilizing Azure Data Factory and manage data in Blob Storage and Cosmos DB
- Collaborate with cross-functional teams; author technical design documentation, deliver project demonstrations, and provide support for production issues
Requirements
- Upto 12 years of experience in software or ML engineering, including at least 2 years with GenAI/LLM applications
- Strong expertise in Node.js and Python, adopting an API-first development approach
- Hands-on experience with Azure OpenAI, LangChain, RAG methodologies, embeddings, and Azure Cognitive Search
- Proficiency with various Azure services: App Service, AKS, Blob Storage, Cosmos DB, Key Vault, Data Factory, Azure ML, Azure DevOps
- Advanced data management skills including ETL, data schema, and SQL; experience with Snowflake or Synapse is advantageous
- Familiarity with security and compliance requirements (PII, GDPR), prompt engineering, and evaluation techniques
Benefits
- flexible (hybrid) work arrangements that empower you to have purposeful time in the office
- career development
Job title
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