Educational Background: Bachelor’s degree in computer science, engineering, a related technical field, or equivalent practical experience, with 2+ years of professional software engineering experience.
Applied AI Experience: Hands-on experience building AI or generative AI features that connect model APIs to business workflows, data, documents, or internal services.
Coding Proficiency: Strong software engineering skills in Python and/or Java, including API development, testing, debugging, asynchronous processing, and maintainable service design.
Cloud Competency: Experience with AWS or equivalent cloud services
RAG and Retrieval Systems: Familiarity with embeddings, chunking, indexing, retrieval strategies, vector and hybrid search, reranking, citations, and vector stores such as OpenSearch, Pinecone, Weaviate, Redis, pgvector, Azure AI Search, or similar technologies.
Agent and Workflow Orchestration: Experience with AI orchestration patterns and tools such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, OpenAI Agents SDK, N8N, AWS Bedrock Agents and Knowledge Bases, or comparable tools.
Structured Outputs and Tool Use: Experience designing prompts, schemas, tool/function calls, workflow contracts, and validation logic so AI systems can produce dependable outputs and interact safely with internal systems.
AI Observability and Evaluation: Familiarity with tracing, monitoring, evals, prompt testing, quality metrics, and debugging tools such as LangSmith, Arize Phoenix, OpenTelemetry, Datadog, Splunk, New Relic, CloudWatch, or comparable platforms.
Communication Skills: Possesses exceptional communication skills, able to turn business requirements into technical tasks, collaborate across teams, and explain AI tradeoffs in clear, practical terms.