QA Engineer responsible for supporting testing activities of telephony software solutions at Saab, ensuring quality standards before deployment. Collaborating with development teams and influencing design decisions throughout product lifecycle.
About the role
- Senior Analyst for AI quality assurance at Dun & Bradstreet. Focusing on GenAI applications and engineering solutions across data validation, reporting, and document processing.
Responsibilities
- Build agentic workflows using Lang Chain/Lang Graph and similar frameworks
- Develop autonomous agents for data validation, reporting, document processing, and domain workflows
- Deploy scalable, resilient agent pipelines with monitoring and evaluation
- Develop GenAI applications using models like GPT, Gemini, and LLaMA
- Implement RAG, vector search, prompt orchestration, and model evaluation
- Partner with data scientists to productionize POCs
- Build distributed data pipelines (Python, PySpark)
- Develop APIs, SDKs, and integration layers for AI-powered applications
- Optimize systems for performance and scalability across cloud/hybrid environments
- Contribute to CI/CD workflows for AI models—deployment, testing, monitoring
- Implement governance, guardrails, and reusable GenAI frameworks
- Work with analytics, product, and engineering teams to define and deliver AI solutions
- Participate in architecture reviews and iterative development cycles
- Support knowledge sharing and internal GenAI capability building
Requirements
- 6-8 years of experience in AI/ML engineering, data science, or software engineering, with at least 2 years focused on GenAI
- Strong programming expertise in Python, distributed computing using PySpark, and API development
- Hands on experience with LLM frameworks (Lang Chain, Lang Graph, Transformers, OpenAI/Vertex/Bedrock SDKs)
- Experience developing AI agents, retrieval pipelines, tool calling structures, or autonomous task orchestration
- Solid understanding of GenAI concepts: prompting, embeddings, RAG, evaluation metrics, hallucination identification, model selection, fine tuning, context engineering
- Experience with cloud platforms (Azure/AWS/GCP), containerization (Docker), and CI/CD pipelines for ML/AI
- Strong problem solving, system design thinking, and ability to translate business needs into scalable AI solutions
- Excellent verbal, written communication and presentation skills.
Job title
Job type
Experience level
Salary
Not specified
Degree requirement
No Education Requirement
Tech skills
Location requirements
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