Artificial Intelligence Engineer
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• Analytical decision-making for AI versus deterministic solutions
• Cloud-native application development
• AI application evaluation methods including test datasets, quality metrics, regression testing, and user feedback
• Technical communication and trade-off analysis
• Collaboration and stakeholder management
• Service design with testing, logging, CI/CD, and deployment practices
• Security-conscious mindset when working with internal or sensitive data Preferred
Qualifications:
• At least five years of professional experience as a full‑stack developer. What will you do?
• Architect and build AI-driven applications that combine large language models, retrieval-augmented generation, agent-style workflows, backend services, APIs and document retrieval pipelines.
• Implement and sustain robust orchestration for LLM integrations, including automated tests, centralized logging, CI/CD pipelines, and runtime monitoring to ensure observability.
• Establish safe, practical patterns for tooling, human approval gates, and controlled agent behavior; advise on model and framework choices with attention to accuracy, cost, latency, maintainability and security.
• Diagnose and resolve common AI system problems such as model hallucinations, poor retrieval relevance, excessive response times, ineffective cost profiles and unstable outputs.
• Work closely with software engineers and product or business stakeholders to embed AI engineering best practices and help shape the technical roadmap. Technologies (mandatory)
• MS Teams
• advanced llm-based ai generation
• RAG Pipeline
• Microsoft 365 Agents SDK
• SAFe Agile
• Logging
• Langgraph
• Langchain
• AI
• Semantic Kernel
• Security
• Microsoft Foundry
• Azure Cloud Platform
• Python
• C#
• product owner
• .
NET / C#
• Microsoft Agent Framework
• Innovation
• Collaboration
• Azure Cloud
• Vector Database Or Enterprise Search Platform
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