3+ years of experience building production systems, including AI / ML applications
Experience building LLM systems using RAG frameworks such as LangChain and LlamaIndex, or custom pipelines and vector stores with Pinecone, Weaviate, FAISS, or OpenSearch Vector Search
Experience designing embedding pipelines, including document ingestion, chunking strategies, indexing, metadata filtering, retrieval optimization, and hybrid search, and designing APIs and services using FastAPI, Flask, or Node.js
Experience with cloud-native architectures, including AWS services such as Bedrock, SageMaker, EKS or ECS, S3, IAM, Lambda, CloudWatch, and Secrets Manager, and agent frameworks and orchestration patterns such as LangGraph, tool calling, and function calling APIs
Experience implementing observability, including logging and tracing with OpenTelemetry, Datadog, or CloudWatch, and metrics pipelines tracking latency, throughput, token usage, cache hit rate, and error rates
Experience optimizing performance using caching layers such as Redis, parallelization or async workflows, and chunking and retrieval tuning, and designing automated evaluation pipelines using benchmark datasets, LLM-as-a-judge techniques, regression testing, and human evaluation workflows
Experience supporting production AI services, including deployment, monitoring, incident response, debugging, and performance tuning, and managing prompt templates, prompt versioning, model configurations, and structured outputs across development and production environments
Knowledge of AI security concepts, including prompt injection, jailbreak resistance, data leakage prevention, guardrails, secure prompt design, and responsible AI practices
Ability to obtain a TS/SCI clearance
Bachelor's degree
Applicants selected will be subject to a security investigation and may need to meet eligibility requirements for access to classified information
