Job Description :

We are seeking a highly skilled Solution Architect Machine Learning - GenAI with a proven track record in large language models, generative AI, cognitive AI, and document processing. This role involves developing and implementing data analyses, data collection systems, and other strategies that optimize efficiency and quality. The ideal candidate will have a strong understanding of Generative AI, Machine Learning, Deep Learning, Natural Language Processing and have experience creating enterprise solutions.

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 in AI 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 like chroma 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 of machine 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 delivering ML architecture patterns operable in native and hybrid cloud architectures.
Research, analyze, recommend, and select technical approaches to address challenging development and data integration problems related to ML Model training and deployment in Enterprise Applications.
Perform research activities to identify emerging technologies and trends that may affect the Data Science/ ML life-cycle management in enterprise application 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.
Developing AI/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 for ML 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.

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