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# Senior AI Engineer

- **Company:** CYNET SYSTEMS INC.
- **Location:** Irvine, CA, United States
- **Experience:** Expert
- **Salary:** $116,480.0 - $126,880.0
- **Contract:** Permanent contract
- **Skills:** LangGraph Framework, Artificial Intelligence, Computer Programming, Data Governance, Data Security, Distributed Systems, Amazon DynamoDB, Graph Database, Python (Programming Language), Knowledge-Based Systems, Redis, Software Safety, Search Technologies, Data Classification, Data Ingestion, Amazon ElastiCache, Retrieval-Augmented Generation, Large Language Models, Prompt Engineering, Apache Spark, Model Validation, Llamaindex, Caching, Reliability of Systems, Generative AI, Backend, Agentic-AI, Build Management, Kubernetes, Information Technology, Deployment Automation, CrewAI, AutoGen, Machine Learning Operations, Api Design, Model Context Protocol, Amazon OpenSearch, GPT, Data Pipelines, Api Management, OpenSearch, Docker, Databricks
- **Published:** November 3, 2026
- **Apply:** https://www.thejobnetwork.com/job/1423f29e-56db-4815-bde9-62dbd361716a/senior-ai-engineer

## About the Role

* 1+ years of experience in Generative AI / LLM systems (RAG, embeddings, prompt engineering).
 * Hands-on experience with AWS ecosystem.
 * Expertise in OpenSearch (vector search), Neptune (graph databases), DynamoDB and Redis (ElastiCache).
 * Strong programming skills (Python preferred).
 * Experience with Databricks and Apache Spark.
 * Solid understanding of data pipelines, distributed systems, and API design., * Experience with model evaluation frameworks and LLM observability tools.
 * AI governance and compliance frameworks.
 * Kubernetes and advanced MLOps practices.
 * Familiarity with Model Context Protocol (MCP) patterns and agent-based architectures.

Skills:

 * Strong problem-solving and analytical thinking.
 * Ability to communicate complex AI concepts clearly.
 * Collaborative and cross-functional mindset.
 * Ownership-driven and proactive execution.

Qualification And Education:

 * Bachelor's or Master's degree in Computer Science, Data Science, AI, or a related field.
 * Proven experience building production-grade AI platforms and systems.
 * Strong background in end-to-end AI/ML lifecycle delivery.

## Description

* Build and operationalize LLM-powered applications using Retrieval-Augmented Generation (RAG), embeddings pipelines, and prompt orchestration frameworks.
 * Design and implement vector search systems using Amazon OpenSearch.
 * Develop graph-based knowledge systems using Amazon Neptune for relationships, lineage, and explainability.
 * Integrate supporting infrastructure such as Amazon ElastiCache (Redis) for session state and caching, and DynamoDB for scalable, low-latency data access.
 * Implement agentic workflows using frameworks such as LangGraph, AutoGen, CrewAI (or equivalent).
 * Integrate with LLM frameworks like LangChain, LlamaIndex for tool calling, retrieval orchestration, and context management.
 * Define standards for tool integration and context-sharing patterns.
 * Evaluate LLM models and retrieval strategies across latency, cost, accuracy, and context limitations.
 * Design and build scalable data pipelines using Databricks and Apache Spark.
 * Implement data ingestion and transformation pipelines, document processing, and embedding generation.
 * Ensure high data quality standards including validation, completeness, consistency, and monitoring.
 * Implement data governance frameworks including data classification, access controls, retention policies, and auditability.
 * Develop backend services exposing AI capabilities through secure and scalable APIs.
 * Define best practices for API contracts, reliability, and enable reusability of platform capabilities.
 * Build and manage CI/CD pipelines for AI and data workloads.
 * Deploy production systems using Docker and Kubernetes.
 * Implement deployment strategies including blue/green deployments, canary releases, rollback strategies, and feature flags.
 * Ensure system reliability through monitoring, alerting, observability, and secrets management.
 * Define and track GenAI quality metrics including grounding, retrieval relevance, response consistency, latency, and cost per request.
 * Implement prompt/version tracking and continuous improvement workflows.
 * Implement secure AI systems with access control, data protection policies, and responsible AI guardrails.
 * Ensure compliance with best practices in AI safety, data privacy, monitoring, and auditability.