Technical Architect-AI ML

Solution Architect Machine Learning - GenAI with a proven track record in large language models, generative AI, cognitive AI, and document processing

Required Skills:

• 12-16 Years Significant experience in Python and its libraries for data science, Generative AI, and Cognitive AI solutions.
• Minimum of 7 years of experience as a Data Scientist/ML Engineer Expertise in Natural Language Processing, Computer Vision, or Predictive Analytics Proven experience in developing enterprise solutions leveraging AI.
• A masters degree in computer science, Data Science, Mathematics, or a related field. A PhD is preferred.
• Experience inAI solution development on Azure with Azure native services like cognitive services, cognitive search, serverless azure functions, blob storage etc.
• Experience with Prompt Engineering, frameworks like langchain, llama-index and vector databases likechroma DB, weaviate, pinecone etc
• Experience working with structured databases (SQL) and unstructured databases (Mongo DB, Cosmos DB)
• Experience with architecting use-cases related to Conversational AI or Vision AI or AI enabled automation.
• Experience with building knowledge bases with structured and unstructured data
• Strong understanding ofmachine learning techniques and algorithms.
• Knowledge of a variety of machine learning techniques (clustering, decision tree learning, artificial neural networks, etc.) and their real-world advantages/drawbacks.
• Excellent written and verbal communication skills for coordinating across teams.

Key Responsibilities:

• Defining, designing, and deliveringML architecturepatterns operable in native and hybrid cloud architectures.
• Research, analyze, recommend, and select technical approaches to address challenging development and data integration problems related toML Model training and deployment in Enterprise Applications.
• Perform research activities to identify emerging technologies and trends that may affect theData Science/ ML life-cycle management in enterpriseapplication portfolio.
• Leading project teams to develop and implement enterprise solutions for document processing, utilizing cutting-edge technologies like large language models and generative AI including agent based systems.
• Designing and building Knowledge bases and implementing cognitive AI solutions to optimize the efficiency and quality of document analysis.
• DevelopingAI/ML models in real-world environments and integrating AI/ML using Cloud native or hybrid technologies into large-scale enterprise applications.
• In-depth experience in AI/ML and Data analytics services offered on AWS and/or Microsoft Azure cloud solution and their interdependencies.
• Demonstrated experience developing best practices and recommendations around tools/technologies forML life-cycle capabilities such as Data collection, Data preparation, Feature Engineering, Model Management, MLOps, Model Deployment approaches and Model monitoring and tuning.
• Collaborating with cross-functional teams, including engineering and product development teams.
• Presenting and explaining information in an accessible way to non-technical team members and stakeholders.