Senior QA Engineer Manual and Automation (AI)

Senior QA Engineer Manual and Automation

Company

WaveBL

Conditions

19 hours agoLead Ai Jobs by WaveBL

WaveBL WaveBL provides a blockchain-based electronic trade-document platform centered on electronic bills of lading. It serves freight forwarders, cargo owners, ocean carriers, banks, and customs authorities with secure document transfer, verification, and trade-document management. Series B Company intelligence Distributed Funding history Series B ($8M) Projects Electronic Master Bill of Lading (eMBL) Transport WaveBL for Customs Onchain Compliance and Investigations WaveBL Digital Hub Transport About WaveBL WaveBL operates a blockchain-based platform for managing and transferring electronic bills of lading and other trade documents. Its Digital Hub stores and extracts data from documents, supports automated document generation, search, error detection, compliance verification, and tracking. The platform uses peer-to-peer transfer, encryption, digital signatures, anonymization, and a distributed ledger to protect document authenticity, integrity, confidentiality, and transferability. WaveBL markets its solutions to freight forwarders, beneficial cargo owners, ocean carriers, banking institutions, and customs authorities. View jobs by WaveBL

Skills

A/B Testing Aws Azure Data Pipeline Gcp Information Retrieval Java Kafka Llm Machine Learning Model Evaluation Multimodal Model Nlp Nosql Prompt Engineering Python Rag Sql

About the Role

You will design and build scalable data pipelines for AI workflows. You will integrate and operationalize LLMs and multimodal models, evaluate and optimize their performance, and develop Java and Python backend services. You will implement RAG and information-retrieval pipelines, improve outputs through prompt engineering and A/B testing, and turn AI capabilities into end-user features.

Requirements

• 7+ years of hands-on software engineering experience
• Java proficiency
• Python proficiency
• Cloud environment experience with AWS, GCP, or Azure
• Kafka experience
• SQL knowledge
• NoSQL database knowledge
• Machine learning fundamentals
• Natural language processing knowledge
• Information retrieval knowledge
• RAG architecture familiarity
• Data pipeline experience for training, evaluation, or experimentation
• Prompt optimization, model evaluation, and cost-aware experimentation experience

Responsibilities

• Design and build scalable data pipelines for AI workflows
• Integrate and operationalize LLMs and multimodal models
• Conduct error analysis, model evaluation, and cost and performance optimization
• Develop and maintain Java and Python backend services
• Implement retrieval-augmented generation and information-retrieval pipelines
• Apply prompt engineering, validation, and A/B testing
• Collaborate with product and engineering teams to turn AI capabilities into end-user features
• Stay current with emerging AI trends and tools

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