Senior AI Engineer (GenAI + Data Platform – AWS)

American IT Systems•Irvine, CA

1d•Hybrid

About The Position

We are seeking a Senior AI Engineer to design, build, and scale a production-grade Generative AI and Data Platform on AWS. The role focuses on enabling LLM-powered capabilities through vector search, graph-based knowledge systems, and governed data pipelines. The ideal candidate will own end-to-end delivery across the AI lifecycle, including: Data ingestion and knowledge curation, Embeddings and retrieval systems, Backend services and APIs, CI/CD pipelines and deployment. This role will closely partner with product and engineering teams to operationalize AI capabilities in externally facing applications and drive evolution toward agentic AI systems.

Requirements

Strong experience in Generative AI / LLM systems (RAG, embeddings, prompt engineering).
Hands-on experience with AWS ecosystem.
Expertise in OpenSearch (vector search).
Expertise in Neptune (graph databases).
Expertise in DynamoDB and Redis (ElastiCache).
Experience with LangChain / LlamaIndex.
Experience with Agentic AI frameworks (LangGraph, AutoGen, CrewAI).
Strong programming skills (Python preferred).
Experience with Databricks and Apache Spark.
Solid understanding of Data pipelines.
Solid understanding of Distributed systems.
Solid understanding of API design.
Experience with Model evaluation frameworks and LLM observability tools.
Experience with AI governance and compliance frameworks.
Experience with Kubernetes and advanced MLOps practices.
Familiarity with Model Context Protocol (MCP) patterns.
Familiarity with Agent-based architectures.
Bachelor's or Master's degree in Computer Science / Data Science / AI / related field.
Proven experience building production-grade AI platforms and systems.
Strong background in end-to-end AI/ML lifecycle delivery.

Nice To Haves

AWS Certified Solutions Architect
AWS Certified Machine Learning Specialty
AWS Data Engineer Certification

Responsibilities

Build and operationalize LLM-powered applications using Retrieval-Augmented Generation (RAG), Embeddings pipelines, and Prompt orchestration and evaluation 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: 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 (tool calling, retrieval orchestration, context management).
Define standards for Tool integration and Context-sharing patterns (MCP-style designs).
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 (chunking, metadata tagging), and Embedding generation and indexing.
Ensure high data quality standards: Validation, completeness, consistency, monitoring.
Implement data governance frameworks: Data classification and access controls, Retention policies, Auditability and lineage tracking.
Develop backend services exposing AI capabilities through secure and scalable APIs.
Define best practices for API contracts and versioning, Reliability (retry logic, circuit breakers, idempotency).
Enable reusability of platform capabilities across teams and applications.
Build and manage CI/CD pipelines for AI and data workloads.
Deploy production systems using Docker (containerization) and Kubernetes (orchestration).
Implement deployment strategies: Blue/green deployments, Canary releases, Rollback strategies, Feature flags.
Ensure system reliability through Monitoring (latency, failures, cost, data freshness), Alerting and observability, Secrets management and least-privilege access.
Optimize platform performance and cost.
Define and track GenAI quality metrics: Grounding / faithfulness, Retrieval relevance, Response consistency, Latency and cost per request.
Implement Prompt/version tracking, Offline evaluation pipelines, and Continuous improvement workflows.
Implement secure AI systems with Access control and authentication, Data protection policies, and Responsible AI guardrails.
Ensure compliance with best practices in AI safety, Data privacy, Monitoring and auditability.