AI Engineer

Cotecna is a leading provider of testing, inspection and certification services. Founded in Switzerland in 1974, Cotecna started off as a family business and has now grown to become a world-class international player with over 7000 employees in more than 140 offices across 50 countries.

Mission of the Role

Design, build, and productionize enterprise-grade AI solutions powered by Large Language Models (LLMs), Agentic AI frameworks, and Retrieval-Augmented Generation (RAG) architectures. Own the complete lifecycle from proof of concept to scalable, secure, and highly reliable production deployment.

Key Responsibilities

Build, deploy, and optimize LLM-powered applications, agentic workflows, and RAG-based solutions for enterprise use cases.

• Design and implement scalable AI services leveraging Azure OpenAI, Claude, and other foundation model APIs.
• Develop robust prompt engineering strategies, structured outputs, function/tool calling, and agent orchestration patterns.
• Create end-to-end RAG pipelines, including document ingestion, chunking, embeddings, vector search, re-ranking, evaluation, and monitoring.
• Ensure production reliability through observability, performance optimization, token management, retries, streaming responses, and fault-tolerant architectures.
• Implement Responsible AI practices, including PII protection, prompt injection mitigation, output validation, cost controls, and latency guardrails.
• Develop and integrate AI services using Azure Functions, Service Bus, Key Vault, Document Intelligence, and REST APIs.
• Collaborate with cross-functional teams to convert AI POCs into scalable production-grade solutions.
• Drive continuous experimentation, model evaluation, and innovation in Generative AI and Agentic AI technologies.

Qualifications, Experience And Technical Skills

• .NET / C# integration experience and interoperability with backend systems.
• Experience with MongoDB, Redis, and event-driven architectures.
• Knowledge of model fine-tuning techniques, including LoRA.
• Expertise in embedding model evaluation, selection, and optimization.
• Hands-on experience with multimodal AI solutions involving vision and speech models.
• Familiarity with evaluation frameworks such as Ragas, Promptfoo, and Azure AI Evaluation SDK.
• Experience with containerization and cloud-native deployments using Docker, AKS, and Azure Container Apps.
• Exposure to Human-in-the-Loop (HITL) workflows and AI-assisted user interfaces.

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