Lead Data Scientist / AI Solution Architect

You are being offered a role at Optimum Data Analytics (ODA) as a Lead Data Scientist / AI Solution Architect. At ODA, the focus is on empowering organizations with advanced data and AI solutions tailored to real-world challenges. As part of the AI & Data team, you will play a crucial role in designing and deploying AI/ML solutions with cutting-edge technologies. **Role Overview:** As a Lead Data Scientist / AI Solution Architect at ODA, your responsibilities will include: - Architecting and implementing end-to-end AI/ML solutions using Azure AI/ML services - Leading the design and development of LLMOps pipelines for deploying and managing large language models (LLMs) - Working with containerized deployments using Docker and orchestration tools like Kubernetes - Integrating and fine-tuning Generative AI models for various real-world applications (text, vision, conversation, etc.) - Collaborating with data engineers, product teams, and business stakeholders to deliver scalable AI systems - Driving model monitoring, versioning, and responsible AI practices - Documenting technical architecture, decision-making, and knowledge transfer **Key Responsibilities:** - Architect and implement end-to-end AI/ML solutions using Azure AI/ML services - Lead the design and development of LLMOps pipelines for deploying and managing large language models (LLMs) - Work with containerized deployments using Docker and orchestration tools like Kubernetes - Integrate and fine-tune Generative AI models for various real-world applications (text, vision, conversation, etc.) - Collaborate with data engineers, product teams, and business stakeholders to deliver scalable AI systems - Drive model monitoring, versioning, and responsible AI practices - Document technical architecture, decision-making, and knowledge transfer **Qualifications Required:** - 6 to 8 years of experience in Data Science / AI / ML solution development - Strong hands-on experience with Azure AI services (ML Studio, Azure OpenAI, etc.) - Experience working with LLMs, prompt engineering, and fine-tuning techniques - Proven experience with containerized environments (Docker, Kubernetes) - Strong programming skills in Python with relevant ML/AI libraries (TensorFlow, PyTorch, Transformers, etc.) - Solid understanding of LLMOps/MLOps lifecycle and best practices - Familiarity with CI/CD pipelines, Git, and DevOps for ML - Excellent communication, stakeholder management, and team collaboration skills **Additional Details:** Unfortunately, there are no additional details about the company provided in the job description.,