Description

Key Technical Requirements:

• Strong proficiency in Python, including asynchronous programming, type annotations, and automated testing.
• Hands-on experience with LLM/Generative AI application development.
• Strong experience in Retrieval-Augmented Generation (RAG), embedding models, indexing strategies, and retrieval optimization.
• Experience with at least one LLM orchestration framework: LangGraph, LangChain, LlamaIndex, Semantic Kernel, AutoGen, or Amazon Bedrock Agents.
• Expertise in LLM evaluation methodologies, golden datasets, evaluation metrics, and LLM-as-a-judge techniques.
• Experience developing cloud-based services, REST APIs, data pipelines, and event-driven workers.
• Knowledge of AI observability, tracing, structured logging, quality monitoring, and cost optimization.
• Exposure to enterprise security, compliance, and governance requirements.

Preferred Technical Skills:

• FastAPI, Pydantic, PostgreSQL, Aurora PostgreSQL, and vector extensions.
• AgentCore and Agentic AI.
• Kubernetes, Helm, Terraform, and CI/CD.
• Apache Kafka and event-driven architecture.
• Datadog and OpenTelemetry.
• Document AI, OCR, intelligent document processing, and human-in-the-loop workflows.
• Experience in financial services, insurance, healthcare, or other regulated industries.

Experience Requirements

• Minimum 5+ years of overall software engineering experience.
• Minimum 2+ years of hands-on LLM/Generative AI development experience.
• Demonstrated experience deploying at least one enterprise-grade AI/LLM application into production.
• Experience owning and supporting live AI applications, including incident management, root-cause analysis, regression management, and production enhancements.
• Exposure to enterprise security and compliance reviews

Key Skills

Education

Bachelor's degree