Work Mode: Hybrid schedule (2–3 days/week onsite at the company’s Irvine office, 1 day/week onsite at the downtown Los Angeles office, and 1 day/week remote). Responsibilities : Build and operationalize LLM-powered applications using RAG, embeddings pipelines, prompt orchestration, and evaluation frameworks.
Numbers & Facts
Location
Los Angeles, CA
Salary
$55–$59.25 Per Hour
Description
Summary:
Location: Irvine, CA
Work Mode: Hybrid schedule (2–3 days/week onsite at the company’s Irvine office, 1 day/week onsite at the downtown Los Angeles office, and 1 day/week remote)
Responsibilities:
Build and operationalize LLM-powered applications using RAG, embeddings pipelines, prompt orchestration, and evaluation frameworks.
Design and implement vector search solutions using Amazon OpenSearch.
Develop graph-based knowledge systems using Amazon Neptune.
Integrate supporting services including Amazon ElastiCache (Redis) and DynamoDB.
Implement agentic workflows using LangGraph, AutoGen, CrewAI, or similar frameworks.
Work with LangChain and LlamaIndex for retrieval orchestration and context management.
Evaluate models and retrieval strategies based on latency, cost, accuracy, and scalability.
Design and develop scalable data pipelines using Databricks and Apache Spark.
Build ingestion, transformation, document processing, embedding generation, and indexing pipelines.
Establish data quality standards including validation and monitoring.
Implement governance practices including classification, access controls, lineage, and auditability.
Develop secure and scalable backend services exposing AI capabilities.
Define API standards, versioning, resiliency, and reusable platform services.
Build and manage CI/CD pipelines for AI and data workloads.
Deploy and manage applications using Docker and Kubernetes.
Support blue/green deployments, canary releases, rollback strategies, and feature flags.
Implement observability, monitoring, alerting, and cost optimization.
Define and track quality metrics including grounding, retrieval relevance, consistency, latency, and cost.
Implement prompt/version tracking and evaluation pipelines.
Establish secure AI practices including access control, data protection, responsible AI, and compliance standards.
Requirements:
Strong experience with Generative AI and LLM systems (RAG, embeddings, prompt engineering).
Hands-on AWS experience.
Expertise in OpenSearch, Neptune, DynamoDB, and ElastiCache (Redis).
Experience with LangChain, LlamaIndex, LangGraph, AutoGen, or CrewAI.
Strong Python programming skills.
Experience with Databricks and Apache Spark.
Solid understanding of data pipelines, distributed systems, and API development.
Bachelor’s or Master’s degree in Computer Science, Data Science, AI, or related field.
Proven experience building and scaling production-grade AI platforms.