
Senior Artificial Intelligence Expert
1 day ago
We are seeking highly skilled and innovative AI specialists with strong expertise in Azure AI and ML to spearhead cutting-edge projects.
The ideal candidates will bring deep experience in building and deploying advanced machine learning and AI models, particularly working with unstructured data, NLP, and generative AI solutions.
Key Responsibilities- Design, develop, and deploy ML and AI models on the Azure ecosystem.
- Work extensively with unstructured data sets (text, images, documents, etc.) to extract insights and build scalable solutions.
- Apply advanced Natural Language Processing (NLP) and Natural Language Generation (NLG) techniques to solve complex business problems.
- Leverage Copilot Studio, AI Foundry, Genie AI in Databricks, and other Azure-native services for generative AI use cases.
- Collaborate with cross-functional teams to identify AI opportunities and deliver impactful solutions.
- Continuously explore, test, and implement new algorithms, tools, and frameworks to improve model accuracy and performance.
- Communicate findings and recommendations effectively to both technical and non-technical stakeholders.
- Take ownership and accountability for end-to-end project delivery, from data exploration to production deployment.
- Proven hands-on experience in Azure Machine Learning, Azure AI services, and Databricks.
- Strong background in NLP, NLG, LLMs, and generative AI frameworks.
- Experience with unstructured data pipelines and feature engineering.
- Proficiency in Python, PySpark, and ML libraries (TensorFlow, PyTorch, Hugging Face, etc.).
- Practical experience with Copilot Studio, AI Foundry, Genie AI in Databricks is highly desirable.
- Strong problem-solving, analytical, and innovation skills.
- Excellent communication and storytelling ability to present AI solutions to stakeholders.
- Bachelor's or Master's degree in Computer Science, Data Science, AI/ML, or related field.
- Experience in building and deploying enterprise-grade AI solutions in cloud-native environments.
- Prior exposure to GenAI-based copilots, conversational AI, or knowledge assistants.
- Familiarity with MLOps frameworks for productionizing AI models.
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