Interim Senior AI Lead

Extensive hands-on experience with LangChain, LangGraph, and Azure Cognitive Services (Azure OpenAI, AI Search, Document Intelligence). Has successfully designed and implemented scalable RAG architectures and Agentic AI solutions

Brings strong skills in Python, Flask, Spark Streaming, and Azure-native architectures. Combined with expertise in CI/CD, Docker, Kubernetes, Terraform, SQL/NoSQL databases

Pioneered LLM-powered RAG integrations, Graphbased fraud detection, and Agentic AI for Cypher translation. Track record in turning cutting-edge AI concepts into production-ready solutions

Has deep Scrum experience and has led workshops, POCs, and pre-sales engagements, consistently bridging the gap between development teams, business stakeholders, and product management. Can seamlessly contribute to agile sprints, feedback loops, and continuous AI feature improvement

Led workshops on Graph RAG, ML integration and vector indexing for advanced graph analytics

Revealed high-risk user clusters with graph ML, streamlining fraud detection and reducing manual checks by 60%

Pioneered LLM-powered Graph RAG integration for presales, accelerating sales cycles and generating 40+ leads

Engineered Agentic AI Cypher translation, automating queries and driving product enhancements

Designed a fraud detection layer using graph features such as betweenness and PageRank to identify collusion and account takeovers

Featured as speaker at Data & AI and Graph Summits, presenting reference architectures for Graph RAG

Led POCs and roadmaps accelerating production deployments and meta-layer solution development

Collaborated with product management, applying product sense to ideate, incubate and scale AI products

Developed a secure real-time streaming architecture with Spark, improving security and speed for ML applications

Demonstrated strategic thinking in planning and executing AI projects for impactful business outcomes

Spearheaded team development initiatives, fostering collaboration and performance within AI projects

Served as a customer-facing advisor, aligning product strategy with client goals and leading cloud architecture delivery

Provided technical guidance in solution design and disaster recovery concepts

Reduced annual attrition by 2% using ML churn models aligning employee goals with business value

Automated repair reports with OCR and topic modeling, extracting insights from 1M+ documents via AWS SageMaker

Engaged stakeholders to align AI initiatives with organizational goals

Engineered Kubernetes deployments with secure data management and robust data governance

Built a machine learning maintenance system with advanced analytics and preprocessing, cutting repairs by 20%

Conducted research and developed a PNN architecture, improving transfer learning accuracy by 17%

Reduced data needs via active learning strategies

Improved operational efficiency by analyzing data simulations and aligning them with technical requirements

Applied design of experiments (DOE) and ML algorithms to optimize industrial processes