Gen AI/ML Solution Architect

• Lead end-to-end Gen AI/ML solution architecture - from problem definition, data acquisition, and feature engineering to model deployment and monitoring.
• Collaborate with business stakeholders to define requirements and translate them into scalable AI/ML solutions.
• Design and implement advanced NLP systems using state-of-the-art models (BERT, ELMO, word2vec) for tasks such as sentiment analysis, named entity recognition, and topic modeling.
• Build and integrate predictive models, leveraging cloud-based platforms such as Azure Machine Learning for forecasting and analytics.
• Develop Retrieval-Augmented Generation (RAG) pipelines for intelligent document retrieval and question-answering systems.
• Implement personalized recommendation engines using cutting-edge frameworks (e.g., Semantic Kernel).
• Ensure solutions are optimized for performance, scalability, and maintainability in production environments.
• Mentor and guide teams on AI/ML best practices, frameworks, and emerging technologies.

• Minimum 2-3 Project on GEN AI LLM Architect
• Machine Learning with MLOps
• Convert business problems to solutions, * Bachelor’s in Engineering or Computer Science and a Master’s in Information Technology, Telecommunications, or a related field.
• 14+ years of professional experience in AI, data mining, deep learning, predictive analytics, and machine learning.
• Proven track record in the full data science project lifecycle, including data wrangling, statistical analysis, and data visualization.
• Proficiency in Python and R, with expertise in AI/ML libraries such as TensorFlow, PyTorch, Scikit-learn, Transformers, and visualization tools (Matplotlib, Seaborn, ggplot2).
• Strong knowledge of NLP techniques and frameworks, vector databases, and MLOps workflows.
• Experience with cloud-based AI platforms (Azure ML, AWS Sagemaker, or GCP AI Platform).
• Solid understanding of cognitive AI systems, intelligent automation, and decision-support systems.

Preferred Skills:

• Experience with Retrieval-Augmented Generation (RAG) pipelines and Semantic Kernel integration.
• Expertise in deploying AI solutions at enterprise scale with robust API integrations.
• Strong communication and leadership skills for cross-functional collaboration.