Experience:

• Proven track record in shipping products and developing state-of-the-art Gen AI product architecture
• 10+ years of experience with a strong balance of business acumen and technical expertise in AI
• 5+ years in building and releasing NLP/AI software, with specific experience in RAG , Agents systems and embedding models
• Demonstrated experience in delivering Gen AI products, including Multi-modal LLMs, Foundation models, and agentic AI systems
• Deep familiarity with cloud technologies, especially Azure, and experience deploying models for large-scale inference using advanced LLM ops techniques

Qualification:

• Bachelor’s or master’s degree in computer science, Data Science, or a related technical field
• Demonstrated ability to translate complex technical concepts into actionable business strategies
• Experience in data-driven decision-making processes
• Strong communication skills, with the ability to collaborate effectively with both technical and non-technical stakeholders
• Proven track record in managing and delivering AI/ML projects, with a focus on GenAI solutions, in large-scale enterprise environments

Additional responsibilities:

• Apply advanced software engineering and DevOps principles, utilizing tools like Git, Kubernetes, and CI/CD for efficient LLM ops
• Collaborate across teams to ensure AI platforms meet both business and technical requirements
• Spearhead the exploration and application of cutting-edge Large Language Models (LLMs) and Generative AI
• Oversee MLOps, automating ML pipelines from training to deployment
• Engage in sophisticated model development from ideation to deployment
• Proactively reduce biases in model predictions, focusing on fair and inclusive AI systems
• Design efficient data pipelines to support large language model training and inference

More skills:

SLM, LAM, cloud platforms, Agents systems, embedding models, NLP/AI

Other:

• Excellent communication and interpersonal skills, with a strong design capability and ability to articulate complex AI concepts to diverse audiences
• Hands-on experience with chunking strategies, RAG implementation, and optimizing embedding models for various AI applications