> Markdown version of [/jobs/ext/1270009-senior-ai-engineer](https://www.wearedevelopers.com/jobs/ext/1270009-senior-ai-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior AI Engineer - **Company:** Flex Ltd - **Location:** Irvine, CA, United States - **Experience:** Expert - **Salary:** $97,760.0 - $106,080.0 - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Automated Storage and Retrieval Systems, Audit Trail, User Authentication, Big Data, Cloud Computing, Encodings, Information Engineering, Data Governance, Data Infrastructure, Distributed Systems, Amazon DynamoDB, Graph Database, Python (Programming Language), Knowledge-Based Systems, Machine Learning, Redis, Azure Machine Learning, Search Technologies, Management of Software Versions, Data Ingestion, Amazon ElastiCache, System Availability, Large Language Models, Prompt Engineering, Apache Spark, Model Validation, Generative AI, Backend, Build Management, AI Platforms, Kubernetes, Information Technology, Data Lineage, AWS Data Analytics, Machine Learning Operations, Virtual Agents, Api Design, Restful APIs, GPT, Data Pipelines, Docker, Databricks, Microservices - **Published:** July 15, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=b8c2f67e0c584fc3 ## About the Role * 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., * Bachelor's or Master's degree in Computer Science, Data Science, Artificial Intelligence, or a related field. * Proven experience building production-grade AI platforms and systems. * Strong background in end-to-end AI/ML lifecycle delivery. Domain Experience * AI/ML Platform Engineering * Generative AI / LLM Applications * Data Platform / Big Data Engineering Preferred Certifications * AWS Certified Solutions Architect * AWS Certified Machine Learning - Specialty * AWS Data Engineer Certification Soft Skills * Strong problem-solving and analytical thinking. * Ability to communicate complex AI concepts clearly. * Collaborative and cross-functional mindset. * Ownership-driven and proactive execution. Ready to take the next step? Click "Interested" to kickstart your journey toward a rewarding career!, * Are you willing to work on a contract basis? If not, please hold off on completing the application for now. We'll be posting another opportunity in the future for full time roles. * Do you have a minimum of 5 years of experience with Graph Databases (Amazon Neptune, Knowledge Graphs)? If you do not have, please hold off on completing the application for now. We'll be posting another opportunity in the future for candidates without the required experience. * Do you have a minimum of 5 years of experience with Agentic AI Frameworks (LangGraph / AutoGen / CrewAI)? If you do not have, please hold off on completing the application for now. We'll be posting another opportunity in the future for candidates without the required experience. * Do you have a minimum of 5 years of experience with Databricks & Apache Spark (data pipelines, embedding pipelines)? If you do not have, please hold off on completing the application for now. We'll be posting another opportunity in the future for candidates without the required experience. * Do you have a minimum of 5 years of experience with Backend/API Development (Python, scalable APIs, microservices)? If you do not have, please hold off on completing the application for now. We'll be posting another opportunity in the future for candidates without the required experience. * Do you have a minimum 5 years of experience with Generative AI / LLM (RAG, embeddings, prompt engineering)? If you do not have, please hold off on completing the application for now. We'll be posting another opportunity in the future for candidates without the required experience. * Do you have a minimum of 5 years of experience with AWS Cloud (OpenSearch, Neptune, DynamoDB, ElastiCache/Redis)? If you do not have, please hold off on completing the application for now. We'll be posting another opportunity in the future for candidates without the required experience. * Do you have a minimum of 5 years of experience with Vector Search & Retrieval Systems (OpenSearch / Vector DB)? If you do not have, please hold off on completing the application for now. We'll be posting another opportunity in the future for candidates without the required experience. ## Description Please note: Only candidates who meet all listed required qualifications will be considered for this position. * Generative AI / LLM (RAG, embeddings, prompt engineering) * AWS Cloud (OpenSearch, Neptune, DynamoDB, ElastiCache/Redis) * Vector Search & Retrieval Systems (OpenSearch / Vector DB) * Graph Databases (Amazon Neptune, Knowledge Graphs) * LLM Frameworks (LangChain / LlamaIndex) * Agentic AI Frameworks (LangGraph / AutoGen / CrewAI) * Databricks & Apache Spark (data pipelines, embedding pipelines) * Backend/API Development (Python, scalable APIs, microservices) * Strong experience building production-grade Generative AI solutions. * Strong Python programming skills. * Experience with distributed systems, API design, and scalable backend development. * Experience building end-to-end AI/ML platforms. 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 partner with product and engineering teams to operationalize AI capabilities in externally facing applications and drive the evolution toward agentic AI systems., GenAI & Agentic AI * Build and operationalize LLM-powered applications using RAG, embeddings, prompt orchestration, and evaluation frameworks. * Design and implement vector search solutions using Amazon OpenSearch. * Develop graph-based knowledge systems using Amazon Neptune. * Integrate Amazon ElastiCache (Redis) and DynamoDB to support AI applications. * Build agentic workflows using LangGraph, AutoGen, CrewAI, or equivalent. * Integrate LangChain or LlamaIndex for retrieval orchestration, tool calling, and context management. * Define standards for tool integration and context-sharing (MCP-style designs). * Evaluate LLM models and retrieval strategies based on latency, accuracy, cost, and context limitations. Data Engineering * Design and build scalable data pipelines using 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. Backend Development * Develop secure and scalable backend services and APIs. * Define API standards, versioning, reliability, retry logic, circuit breakers, and idempotency. * Build reusable platform capabilities across teams and applications. Deployment & MLOps * Build 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. AI Quality & Security * Define and monitor GenAI quality metrics, including grounding, retrieval relevance, response consistency, latency, and cost. * Implement prompt/version tracking, evaluation pipelines, and continuous improvement workflows. * Ensure AI security through access controls, authentication, data protection, responsible AI guardrails, privacy, and auditability. ## Related Videos - [Coffee with Developers - Maria Apazoglou](https://www.wearedevelopers.com/videos/1209-coffee-with-developers-maria-apazoglou) - [ Evaluating AI models for code comprehension](https://www.wearedevelopers.com/videos/1462-evaluating-ai-models-for-code-comprehension) - [Reducing LLM Calls with Vector Search Patterns - Raphael De Lio (Redis)](https://www.wearedevelopers.com/videos/1714-reducing-llm-calls-with-vector-search-patterns-raphael-de-lio-redis) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [Streaming AI Responses in Real-Time with SSE in Next.js & NestJS](https://www.wearedevelopers.com/videos/1630-streaming-ai-responses-in-real-time-with-sse-in-next-js-nestjs) - [Shipping Faster with Less: Render on Cloud Hosting, AI Workloads, and the Future of DevOps](https://www.wearedevelopers.com/videos/1894-shipping-faster-with-less-render-on-cloud-hosting-ai-workloads-and-the-future-of-devops) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Got AI ideas but no money? Here are 10 free ways to level up your AI skills with Google Cloud](https://www.wearedevelopers.com/magazine/600-got-ai-ideas-but-no-money-here-are-10-free-ways-to-level-up-your-ai-skills-with-google-cloud) - [From Prototype to Production: Build AI Agents with This Free 4-Course Learning Path](https://www.wearedevelopers.com/magazine/655-from-prototype-to-production-build-ai-agents-with-this-free-4-course-learning-path) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it)