We are looking for an AI Engineer / Developer to join our team in transforming our Product Lifecycle Management (PLM) platform through AI-powered features. You’ll be directly involved in the hands-on development and implementation of intelligent solutions that enhance automation, decision-making, and user experience. This role focuses on building, integrating, and optimizing AI/ML models and tools using modern NLP, LLMs, and vector search technologies.
You will work closely with product, engineering teams to deliver meaningful, domain-specific AI capabilities across our PLM ecosystem.

REQUIRED SKILLS & EXPERIENCE

• Strong hands-on expertise in Natural Language Processing (NLP) and LLMs (e.g., GPT, Claude, Gemini, BERT, T5).
• Experience in AI development: chatbots, assistants, virtual agents, etc., using LangChain, Semantic Kernel, or LangGraph.
• Proficiency in building and deploying RAG pipelines, working with prompts (zero-shot, one-shot), and fine-tuning.
• Familiarity with AI search tools such as Azure Cognitive Search, Elasticsearch, or Haystack.
• Solid experience with the Azure AI ecosystem (Azure AI Services, OpenAI on Azure, AKS, etc.).
• Experience with containerization tools: Docker, Kubernetes.
• Working knowledge of MLOps, model versioning, and CI/CD with tools like Azure DevOps or GitHub Actions.

PREFERRED QUALIFICATIONS

• 5+ years of hands-on AI/ML development experience.
• Strong background in backend or full-stack engineering.
• Previous experience delivering AI capabilities in enterprise software.
• Deep familiarity with Azure infrastructure and deployments.
• Strong collaboration skills and ability to communicate effectively with technical teams.

• Develop and integrate AI-powered features using NLP, LLMs, and multimodal AI.
• Build AI-driven solutions to enhance user experience, process efficiency, and decision-making.
• Fine-tune and optimize AI models for performance and accuracy.
• Implement monitoring, testing, and governance around AI features to reduce risks (e.g., prompt injection, bias, drift).
• Collaborate with internal teams to iterate on solutions and improve existing ML pipelines.