Senior AI Engineer

Flex Employee Services seeks a Senior AI Engineer to architect, build, and operate a production-grade Generative AI and Data Platform on AWS, emphasizing LLM-powered capabilities, vector search, and graph-based knowledge systems, all within governed data pipelines. This onsite role in Irvine, CA offers the opportunity to shape scalable AI infrastructure across teams, with a compensation range of $47-$51 per hour and a requirement of five years of experience along with a Bachelor’s or Master’s degree.

• Operationalize LLM-enabled applications using retrieval augmented generation, embeddings, prompt orchestration, and evaluation pipelines.
• Design and implement vector search solutions with Amazon Open Search.
• Develop graph-based knowledge systems using Amazon Neptune.
• Integrate Redis via Elasti Cache and DynamoDB to support AI applications.
• Build agentic workflows leveraging Lang Graph, Auto Gen, CrewAI, or equivalent frameworks.
• Incorporate Lang Chain or Llama Index for retrieval orchestration, tool invocation, and context management.
• Define standards for tool integration and context-sharing using MCP-style designs.
• Evaluate LLM models and retrieval strategies based on latency, accuracy, cost, and context limits.
• Design and scale data pipelines with Databricks and Apache Spark.
• Develop data ingestion, transformation, document processing, embedding generation, and indexing pipelines.
• Ensure data quality through validation, completeness, consistency, and monitoring.
• Implement data governance, access controls, retention policies, auditability, and lineage tracking.
• Develop secure and scalable backend services and APIs.
• Define API standards, versioning, reliability, retry logic, circuit breakers, and idempotency practices.
• Build reusable platform capabilities for multiple teams and applications.
• Develop and manage CI/CD pipelines.
• Deploy production systems using Docker and Kubernetes.
• Implement blue/green deployments, canary releases, rollback strategies, and feature flags.
• Monitor platform reliability, observability, security, data freshness, and cost optimization.
• Define GenAI quality metrics covering grounding, retrieval relevance, response consistency, latency, and cost.
• Implement prompt and version tracking, evaluation pipelines, and continuous improvement workflows.
• Ensure AI security through access controls, authentication, data protection, responsible AI guardrails, privacy, and auditability.

• Generative AI / LLM capabilities including RAG, embeddings, and prompt engineering.
• AWS Cloud expertise with Open Search, Neptune, DynamoDB, and Elasti Cache/Redis.
• Vector search and retrieval systems experience (Open Search or Vector DB).
• Graph databases and knowledge graphs (Amazon Neptune).
• LLM frameworks such as Lang Chain and Llama Index.
• Agentic AI frameworks like Lang Graph, Auto Gen, or CrewAI.
• Databricks and Apache Spark for data and embedding pipelines.
• Backend/API development in Python with scalable APIs and microservices.
• Proven experience delivering production-grade Generative AI solutions.
• Strong Python programming skills and experience with distributed systems, API design, and scalable backend development.
• Experience building end-to-end AI/ML platforms.
• Bachelor’s or Master’s degree in Computer Science, Data Science, Artificial Intelligence, or related field.
• Demonstrated track record of delivering production AI platforms and systems.
• Solid background in end-to-end AI/ML lifecycle delivery.

• Lang Chain, Llama Index, Lang Graph, Auto Gen, CrewAI
• Open Search, Amazon Neptune, DynamoDB, Elasti Cache (Redis)
• Databricks, Apache Spark
• Python
• Docker, Kubernetes

• Dental insurance
• Health insurance
• Referral program
• Vision insurance

• Model evaluation frameworks and LLM observability tools
• AI governance and compliance frameworks
• Kubernetes and advanced MLOps practices
• Model Context Protocol (MCP) patterns
• Agent-based architectures

• AI/ML Platform Engineering
• Generative AI / LLM Applications
• Data Platform / Big Data Engineering

• Strong problem-solving and analytical thinking
• Ability to communicate complex AI concepts clearly
• Collaborative and cross-functional mindset
• Own…