Pune District, Maharashtra, India
Enterprise AI Transformation Leader specializing in Generative AI, Agentic AI, and AI Platform Modernization with 10+ years of experience driving scalable AI adoption across healthcare, manufacturing, analytics, and intelligent automation domains. Proven track record of architecting and leading enterprise-scale AI initiatives that improve operational efficiency, accelerate decision-making, modernize business workflows, and deliver measurable business impact through production-grade AI systems. Expertise spans Generative AI, Agentic AI, Multimodal RAG, LLMOps, AI orchestration, cloud-native AI platforms, predictive analytics, and intelligent automation using GPT-4o, Claude, Gemini, LangGraph, CrewAI, Azure AI, AWS Bedrock, and Vertex AI. Known for bridging executive AI strategy with deep technical execution — helping organizations move from AI experimentation to scalable enterprise deployment and AI-driven transformation. Experienced in leading cross-functional AI programs, mentoring high-performing engineering teams, influencing executive stakeholders, and building reusable AI frameworks that accelerate innovation and enterprise AI adoption. Speaker, mentor, and AI innovation advocate passionate about building practical, responsible, and business-aligned AI systems that create long-term organizational impact. Interests: Generative AI | Agentic AI | Enterprise AI Strategy | AI Platforms | LLMOps | Multimodal AI | AI Transformation | AI Governance | Intelligent Automation | Digital Twins | Applied AI Leadership Contact: Reach out at [email protected] for professional opportunities. Connect with me to explore how we can innovate together.
Lead Generative AI, Agentic AI, and LLM initiatives across healthcare transformation programs, covering RAG chatbots, enterprise knowledge assistants, customer email automation, and cloud-native agentic AI platforms. Manage a 7-member team across solution design, delivery execution, stakeholder alignment, and AI/ML system integration with business goals. Agentic RAG-Based Enterprise Knowledge Assistant • Designed a LangGraph-based enterprise knowledge assistant using User Intent, Retrieval, Answer Generation, Validation, and Tool Execution agents. • Enabled contextual knowledge retrieval, validated answer generation, and tool-based execution to improve enterprise knowledge access and decision-making. • Reduced manual knowledge-search effort by an estimated 50–60% for repetitive internal support and documentation queries. Agentic AI-Based Customer Email Automation System • Designed an agentic AI platform to classify and process customer emails across invoices, shipments, work orders, repairs, breakdowns, and order-related queries. • Built multi-agent workflows for classification, entity extraction, validation, Salesforce/SAP lookup, response generation, and human escalation. • Targeted 60–70% reduction in manual email triage and reduced standard inquiry turnaround time from hours to minutes. Cloud-Native Agentic Financial Analysis Platform • Built a cloud-native agentic AI platform using Google ADK and Gemini 2.5 Pro to orchestrate SQL and RAG agents for financial analysis. • Automated product-level expense analysis, financial reporting, insight generation, and explainable stakeholder-ready reports.
Gen AI-Powered Visual Chatbot for Rapid Assistance Developed an advanced system to generate videos from documents. Utilizing Multimodal RAG, the system dynamically produces video responses by integrating contextually relevant images and synchronized audio. This solution leverages sophisticated image retrieval and text-tospeech synthesis, enhancing information accessibility and engagement, while optimizing content creation workflows and reducing operational costs for businesses. Multimodal RAG, LlamaIndex, Lance DB, GPT 4o Survival Analysis for Product Reliability: Achieved an 83% reduction in runtime for product reliability analysis using Survival Analysis (Weibull Distribution) by developing and deploying efficient model-building code. Measured success through enhanced workflow and productivity, setting a new benchmark for automated life data analysis. Successfully eliminated software dependencies, resulting in a substantial cost savings of USD 65,000 in license fees. Software Fault Prediction and Risk Mitigation: Pioneered a comprehensive risk quantification and mitigation approach throughout various phases of the software development lifecycle. Implemented a machine learning-based software defect prediction model, reducing development costs by detecting faults earlier. Improved software quality, reliability, and efficiency through the creation of a robust bug prediction model using ML algorithms. Demonstrated expertise in understanding key factors influencing software performance. Document Focused Chatbot: Innovated and developed a chatbot tailored for document-related queries, significantly enhancing user experience. Utilized advanced techniques such as embeddings using sentence structure to enable the chatbot to extract relevant information from documents. Transformed the user interaction experience by efficiently retrieving important information from documents and providing precise answers.
1. Enhancing User Experience through NLP: Accomplished: Developed an NLP model to discern user sentiments, measured by a 20% increase in user engagement. Methodology (Y): Analyzing user reviews with advanced NLP techniques. Impactful Action (Z): Executed comprehensive text preprocessing and exploratory data analysis, employing techniques such as lowercasing, stop word removal, stemming, lemmatization, and tokenizer. Implemented text vectorization methods like Bag of Words and Tf-IDF. Utilized Word Clouds for insightful exploratory data analysis. Result: Identified key areas for product and service improvement, contributing to enhanced user satisfaction. 2. Aerospace Application Simulation Efficiency: Accomplished: Developed a transfer function to predict hose fitting failure, resulting in a 60% reduction in data acquisition time. Methodology (Y): Utilizing design of experiment (DOE) methods for efficient simulation. Impactful Action (Z): Generated data points for simulation, saving 60% of the time previously required for data acquisition. This led to a significant reduction in the time needed for new product launches. Result: Improved efficiency in aerospace application simulations, ensuring timely product launches and reduced costs. 3. Boosting Conversion Rate in Aerospace Sales: Accomplished: Improved conversion rate from 30% to 70% for new enquiries in the aerospace sector. Methodology (Y): Developing a model to identify promising enquiries with high conversion potential. Impactful Action (Z): Managed null values in categorical and numerical columns using techniques like Simple Imputer and KNN Imputer. Implemented dimensionality reduction techniques such as PCA, Variance Inflation Factor, and p-value analysis. Result: Successfully increased the conversion rate, optimizing the sales process and enhancing revenue generation.
Open-Source TPMS Geometry Synthesis Tool | Python · NumPy · Scikit-Image · NumPy-STL Built a Python pipeline to generate 3D-printable Triply Periodic Minimal Surfaces (TPMS) directly from implicit mathematical functions — no CAD, no proprietary tooling. Supports any TPMS geometry (Gyroid, Schwartz P, Diamond) by swapping the implicit function definition. Evaluated user-defined implicit functions f(x,y,z) over a configurable 3D NumPy tensor grid — the same volumetric data structure at the core of 3D CNNs, NeRF, and occupancy network pipelines Extracted watertight triangulated meshes using the Lewiner Marching Cubes algorithm — the identical extraction step used in DeepSDF and Occupancy Networks to convert implicit fields into meshes Engineered dual iso-surface offsetting (±t iso-levels, flipped inner normals) for tunable wall thickness, compatible with FDM, SLA, and metal SLM fabrication workflows Pipeline is architecturally identical to modern neural implicit representation systems — swap the analytical function for a trained MLP and the same Marching Cubes extraction produces neural geometry Stack: Python · NumPy · Scikit-Image · NumPy-STL
Department: Next Generation Machine Development Department, LT6600 3D Group Supervisor: Mrs Yoko Hirono, Assistant Chief Engineer, R&D HQ, Next Generation Machine Development Department, LT6600 3D Group Project Title: Design and Analysis of Jig for Transportation of Cable Control Unit of LASERTEC 6600 3D Collaborated with DMG MORI’s Next Generation Machine Development Department, University of Tokyo, on computational design, structural analysis, and reliability assessment of transportation jig systems for LASERTEC 6600 3D machine components. Performed simulation-driven static, acceleration, and seismic load analysis to evaluate structural integrity, deflection behavior, and transportation safety under real-world operating conditions using computational engineering and theoretical validation methodologies. Focused on computational modeling, design optimization, and intelligent manufacturing system analysis using Creo Parametric 4.0 and STKR 400 structural materials.