Walmart招GenAI&Java岗,LexisNexis大量NLP岗位招人,还有quant intern,sponsor H1B&GC

简历请发送至  jack.krelum 艾特 及迈尔, 都sponsor H1B和绿卡，都remote
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GenAI / NLP Engineer for Lexis Nexis (Remote) （contract to hire)
Need to have a few years doing NLP Engineering (5 years) and then have moved into GenAI the last 2-3 years.
If you have FAANG experience or startup experience or PhD, the number of years experience could be lower.
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We are also hiring non paid quant research intern, fully remote
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Walmart招GenAI&Java岗，long term contract, 需要在Sunnyvale CA onsite，要求不高

补充内容 (2025-10-24 04:08 +08:00):
还有一个
Back End Java - (AI & ML) - Walmart (Hybrid - Sunnyvale, CA)

Walmart is seeking a hands-on Java backend engineer with real AI/ML/GenAI delivery experience. You’ll build and scale JVM-based microservices that power LLM features (chat, retrieval, summarization, agents), owning APIs, data pipelines, and production reliability.
What you’ll do

• Design and ship Java (Java 11+/17, Spring Boot) microservices for AI/ML use cases (RAG, embeddings, inference, evaluations).
• Build secure REST/GraphQL APIs and integrate with LLM providers (Azure OpenAI/OpenAI, Bedrock, Vertex).
• Implement retrieval pipelines: document ingestion, chunking, embeddings, vector stores (pgvector, Pinecone, FAISS, Milvus).
• Orchestrate workflows, prompt versions, tool/function calling, and guardrails (moderation, PII redaction, rate limiting).
• Optimize performance, latency, and cost; add observability (metrics, tracing, logging) and run A/B or offline evals.
• Partner with Product, MLE/Data, and Platform teams; uphold code quality, testing, and CI/CD best practices.

Required qualifications

• 5–8+ years backend engineering with Java and Spring Boot (concurrency, JVM tuning, resiliency patterns, caching).
• 2+ years hands-on with LLMs/GenAI or ML in production, including:
• • Prompt design & function/tool calling; RAG and retrieval pipelines.
  • Embeddings + vector databases (pgvector/Pinecone/FAISS/Milvus).
  • Evaluation/guardrails and incident-aware observability.
• Experience integrating with at least one cloud AI platform: Azure OpenAI, AWS Bedrock, Google Vertex AI, OpenAI APIs.
• Proven design of scalable APIs/microservices, data modeling, and CI/CD (GitHub Actions/Azure DevOps/Jenkins).

Nice to have

• Kafka/Kinesis streaming; Elasticsearch/OpenSearch; Redis caching.
• Data/ML stack: Python for data prep/inference services, Spark, Feature stores.
• Containers & platform: Docker/Kubernetes, service mesh, secrets management.
• Security/compliance for AI systems (prompt/version control, input/output filtering).