| Role Senior AI Engineer GenAI LLM - Location Irvine CA - onsite at Irvine, California, USA |
| Email: [email protected] |
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http://bit.ly/4ey8w48 https://jobs.nvoids.com/job_details.jsp?id=3354435&uid=01d59ff7e1094df89e3489587c5dbe03 From: Isai Bhagyaraj, Scalable systems [email protected] Reply to: [email protected] Hi Hope you are doing well!! I have an urgent requirement with one of my client. Please find the job details below and forward me your updated resume along with your contact details to [email protected] Role: Senior AI Engineer (GenAI / LLM) Location: Irvine, CA - onsite Duration: Long-Term GBaMS ReqID: 10735491/83653-1 10+ years must AI Engineer " 5+ years of experience in software engineering, data engineering, or AI/ML engineering Strong proficiency in Python for AI/data workflows and automation Hands-on experience building solutions in AWS cloud environments Experience with: o Databricks (or similar) and Apache Spark for distributed data processing o OpenSearch / Elasticsearch (including vector search) o Graph databases (Neptune or similar) o DynamoDB and Redis/ElastiCache Experience building backend services and APIs (e.g., Java/Spring Boot, Node.js) Production experience with Docker and Kubernetes Experience with CI/CD pipelines and deployment automation Strong understanding of distributed systems, data architecture, and scalable design ________________________________________ Preferred Qualifications Experience with LLM/GenAI architectures (RAG, embeddings, prompt engineering) Familiarity with LangGraph, AutoGen, CrewAI, or similar agent orchestration frameworks Experience with LangChain or LlamaIndex Experience implementing LLM evaluation and observability frameworks Familiarity with AI security practices and threat models (prompt injection, guardrails) Experience working in regulated environments with strong data governance and compliance requirements ________________________________________ What Youll Own / Impact End-to-end ownership of a modern AI platform powering external-facing digital experiences Establishment of best practices for GenAI integration, evaluation, and security Advancement of the organization toward agentic AI capabilities, with your team leading all related innovation and delivery ________________________________________ Tech Stack (Representative) AWS: Neptune, OpenSearch, DynamoDB, ElastiCache (Redis), IAM, CloudWatch Data: Databricks, Apache Spark AI: LLM integrations, embeddings, vector search, RAG pipelines Agentic/LLM Tooling: LangChain, LlamaIndex, LangGraph, AutoGen, CrewAI Backend: APIs, microservices (e.g., Spring Boot, Node.js) DevOps: Docker, Kubernetes, CI/CD, Infrastructure as Code" "Role Summary We are seeking a Senior AI Engineer to build and scale a production-grade GenAI and data platform on AWS, enabling LLM-powered capabilities through vector search, graph databases, and governed data pipelines. This role owns end-to-end delivery across the AI lifecyclefrom data ingestion and knowledge curation embeddings and retrieval systems backend services and APIs CI/CD and deployment. You will partner closely with product and engineering teams to operationalize AI capabilities in externally facing applications. This position also plays a key role in building and evolving our platform toward agentic systems, including implementing tooling, memory, state management, and guardrails, with all future enhancements and capabilities designed and delivered by our team. ________________________________________ Key Responsibilities GenAI Enablement & Integration Enable LLM-powered use cases using patterns such as retrieval-augmented generation (RAG), embeddings pipelines, prompt orchestration, and evaluation strategies. Design and implement vector-based retrieval systems using Amazon OpenSearch (vector capabilities). Build and maintain graph-based knowledge systems using Amazon Neptune to support entity relationships, lineage, and explainability. Integrate supporting infrastructure: o Amazon ElastiCache (Redis) for low-latency caching and session state o DynamoDB for high-scale, low-latency data access patterns Implement and operate production-grade LLM workflows and agentic patterns using frameworks such as LangGraph, AutoGen, or CrewAI (or equivalent). Integrate LLM application frameworks such as LangChain and/or LlamaIndex, including tool calling, retrieval orchestration, and context management. Establish standards for tool integration and context-sharing patterns (e.g., Model Context Protocol (MCP)-style designs), preparing the platform for Agent Core adoption. Evaluate and compare LLMs and retrieval strategies across latency, cost, accuracy, and context limitations, selecting optimal approaches for production use cases. ________________________________________ Data Pipelines & Knowledge Engineering Design, build, and operate scalable data pipelines using Databricks, including: o Data ingestion and transformation o Document processing (chunking, metadata tagging) o Embedding generation and indexing Build distributed data processing jobs leveraging Apache Spark (Databricks) for large-scale transformation and enrichment. Ensure high standards for data quality, including validation, completeness, consistency, and monitoring. Implement and enforce data governance practices: o Data classification and access controls o Retention policies o Auditability and lineage tracking ________________________________________ Backend Services & APIs Build and maintain backend services that expose AI and data capabilities through secure, scalable APIs. Define service contracts, versioning strategies, and reliability patterns (e.g., retries, circuit breakers, idempotency). Enable reuse of platform capabilities across multiple applications and teams. ________________________________________ Deployment, MLOps & Operational Excellence Own and evolve CI/CD pipelines for AI services and data workloads. Build production-grade AI systems using Docker-based containerization and Kubernetes orchestration. Implement safe deployment strategies (e.g., blue/, canary releases, rollback mechanisms, feature flags). Ensure systems are secure, observable, and reliable, including: o Monitoring and alerting (latency, errors, cost, data freshness) o Secrets management and least-privilege access controls o Performance and cost optimization ________________________________________ LLM Observability, Evaluation & Quality Define and operationalize GenAI quality metrics, including: o Grounding/faithfulness o Retrieval relevance o Response consistency o Latency and cost per request Implement evaluation and observability workflows for LLM and RAG systems, including prompt/version tracking, offline testing, and continuous improvement loops. ________________________________________ LLM Security, Safety & Compliance Implement security controls for LLM-powered systems, including defenses against: o Prompt injection o Data leakage and exfiltration o Unsafe tool execution o Retrieval/data poisoning risks Apply secure-by-design practices aligned to enterprise standards, including least privilege access, audit logging, and data governance controls, particularly within regulated environments. ________________________________________ Agentic Systems Evolution (Future-Focused) Design foundational capabilities to support agent-based architectures, including: o Tool integration patterns o Memory and state management o Guardrails and policy enforcement Drive innovation in agentic systems; all related enhancements will be developed and delivered by this team." Role Descriptions: We are seeking a Senior AI Engineer to build and scale a production-grade GenAI and data platform on AWS| enabling LLM-powered capabilities through vector search| graph databases| and governed data pipelines.This role owns end-to-end delivery across the AI lifecycle from data ingestion and knowledge curation embeddings and retrieval systems backend services and APIs CICD and deployment. You will partner closely with product and engineering teams to operationalize AI capabilities in externally facing applications.This position also plays a key role in building and evolving our platform toward agentic systems| including implementing tooling| memory| state management| and guardrails| with all future enhancements and capabilities designed and delivered by our team. Essential Skills: Seeking a Senior AI Engineer to build and scale a production-grade GenAI and data platform on AWS| enabling LLM-powered capabilities through vector search| graph databases| and governed data pipelines.This role owns end-to-end delivery across the AI lifecycle from data ingestion and knowledge curation embeddings and retrieval systems backend services and APIs CICD and deployment. You will partner closely with product and engineering teams to operationalize AI capabilities in externally facing applications.This position also plays a key role in building and evolving our platform toward agentic systems| including implementing tooling| memory| state management| and guardrails| with all future enhancements and capabilities designed and delivered by our team. Desirable Skills: Keyword: Skills: AI Agents Experience Required: 10+ Hire our IT Recruiter at just $499/month. Keywords: continuous integration continuous deployment artificial intelligence machine learning javascript information technology California Role Senior AI Engineer GenAI LLM - Location Irvine CA - onsite [email protected] http://bit.ly/4ey8w48 https://jobs.nvoids.com/job_details.jsp?id=3354435&uid=01d59ff7e1094df89e3489587c5dbe03 |
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| 03:33 AM 07-May-26 |