kiran

Leading a high performing Global AI Engineering team (India, France, USA) of 25+ Data scientists, ML engineers, UI, Testing for End-to-End Technical delivery of AI Platforms and deployment in Live Productions to different business.
Development of NLP, Computer vision and Prediction novel algorithms as platform for internal customers using AWS & Azure for Data Engineering, Algorithms and Deployments for Finance, HR, Ecommerce, IOT and Industrial IOT, Energy Management, Power domain with visualization in Streamlit/DASH/Quick Sight.
AI Engineering Leader for LLM/ChatGPT/GPT/Chatbot Portfolio Projects using Microsoft Azure Infrastructure (Open API based LLM, Prompt Engineering, LangChain, Vector Database, Natural Language Generation).
Daily chats of 2L+ volume is managed by ML algorithms.
This result in saving the money in tune of worth 200 Crores+ of AI Hub Schneider Electric.
AI driven Energy Management & Decarbonization, IIOT – Smart Manufacturing improve real time monitor, prediction and enhanced decision making.
Cloud & Data Architecting and scaling engineering platform to reduce technical debt and improve performance and agility.
Driving execution of multiple large cross pillar initiatives collaborating with stakeholders.
Building end-to-end systems that integrate distributed systems, combinatorial-optimization systems, and Machine-Learning systems.
Building and mentoring high-performing talent by mentoring Managers, Engineers, Data Scientists.
Defining a long-term talent strategy cutting across technology and business (Functional) roles.
Engaging with global leadership and setting up strategy and priorities for the team and in delivering best data engineering work around the globe.
Adapting and delivering the Agile strategy and creating a collaborative environment across geography for delivering best products to the customers.
Take ownership and responsibility of Budget planning, spend and tracking and promoting Brand Ambassador of Schneider as Industry Leader, Guest Talk in various invited AI conference by supporting company policies, procedures, mission, values, and standards of ethics and integrity.
Providing supervision, mentoring and development opportunities for managers reporting into me.
Making data driven decisions, tracking, and showing continuous improvement in engineering-excellence and operational-excellence metrics.
Defining and executing training and development plan, career plan, goal settings, regular performance reviews and feedback.

E2E deliverable for AI solutions nlp, computer vision, GENAI, ML, DL, . I take ownership of architecture, design and implementation.
Senior AI Engineering Manager/ Associate Director – Artificial Intelligence & DataScience - 20+ yrs. Experience in
Product based Organization
✓ Result-oriented AI Product Engineering Leader with technical expertise and strategic direction for AI
products by incorporating innovative technologies and delivering End to End well-engineered practiced
scalable AI applications. Productionizing of complex AI and Data science solutions in AWS and AZURE cloud
environments with hands-on experience of Product development.
✓ Technical Expertise in Machine Learning, Deep Learning, GEN AI, Optimization Prediction/Forecast,
Quantitative analysis in the field of computer vision, NLP, Time Series Forecasting, Digital Twin
(Simulations) for various domain expertise in Healthcare, Speech, NLP, Text, Image analysis, Remote
sensing, Energy and Power, Biomedical, Fintech, Finance, Pharma, Biotech and IOT/IIOT domain. AI driven
Energy Management & Decarbonization, IIOT – Smart Manufacturing, real time predictions and monitoring
using advanced AI algorithms.
✓ Played Solution Architect, Data Architect, Cloud Architect, Product Manager and Program Manager role
to meet end customers for requirements as well along with Technical Manager for complete End to End
Product development. Design, establish and implement all the analytic activities with a goal to improve
customer experience.
✓ Published thirty journals in International Journals, 1 Patent granted, 2 Patents has been filed and 7
Disclosure is in submitted state. International Reviewer of Springer Journal for AI, ML, DL for Biomedical
Springer Journal. Presented various research papers in various seminars/ conferences/Workshops.
✓ Technical and Managerial leadership, all stages of a project life cycle, collaborator, and as well
independent contributor. Provide thought leadership on solving complex wicked problems through stateof-the-art analytics techniques and providing innovative data-based solutions and extend the reach of the
solution. Financial statements analysis (Balance sheet, P&L) to leadership team for budget analysis.
✓ Own E2E life cycle development and operations of highly scalable platforms/components. Architecting and
scaling engineering platform to reduce technical debt and improve performance and agility. Driving
execution of multiple large cross pillar initiatives co

Establish and grow a Data Science team to be multi-disciplinary and globally aligned with J&J strategy (India, USA).
Lead $500M Supply Chain Analytics Savings projects for Food Manufacturing/Chains/ Retail.
Managed team of 20 Supply Chain Analytics in India Office, from project delivery to resource management.
Project Handling and Enterprise Solution & Cloud Architect for Supply Chain Data Analytics Platform using Machine Learning, Statistical Modelling, Deep Learning.
Time Series Forecasting using R and Python libraries.
Handling End to End Data analytics platform from requirement gathering to azure deployment using Azure Webapp, Azure Data Factory (ADF), Azure storage.
Managing various Data Analytics projects with Microsoft.
Technical Guidance for implementation and support to Microsoft Data science team for their implementation to create Data Science Supply Chain Platform using Azure technology stack.
Architecture – Enterprise & Solution frameworks Integrations - TOGAF's ADM.
Advanced Analytics in Supply Chain Planning Analytics Platform using Operational Research (OR) with RNN (Deep Reinforcement Learning) for Inventory Planning, Order Planning, Replenishment planning, Multi-Echelon Supply Chain Planning.
Presented in papers in international conference on Supply Chain Analytics using Machine Learning & Deep Learning and Winner of J&J Open Data Science Hackathons.
Translate use cases to analytics agenda, including data, approach and validation strategy and develop best practices for data science, considering the full analytical lifecycle with Research innovative techniques, industry best practices, tools, and systems.
Proven ability synthesizing and presenting research/technical findings to diverse audiences.
Experience in mentoring/coaching people and robust team.
Experience with resource planning and allocation within and across organizations with identifying and acquiring global talent needs and resource planning across J&J.

Establish and grow a Data Science team to be multi-disciplinary and globally (India & Netherlands) aligned with Philips Healthcare strategy.
Managing various Data Analytics projects with Microsoft.
Technical Guidance for implementation and support to Microsoft Data science team for their implementation to create Data Science Healthcare AI Technology Platform using Azure technology stack.
Machine learning algorithm for Tumor Tracking analysis based on clinical decision support system using Convolution Neural Networks (Python libraries- NumPy, Pandas, and SciPy).
Patients Similarity analysis to predict the diagnosis and therapy for the based on the past data using MATLAB, Python packages.
Performs Case-based similarity analysis against historical database to query for pediatric patients with similar conditions based on a scoring algorithm and automatically suggest the best therapies based on the highly matching scores of similar patients.
Project outcome converted to Patent Acceptance.
Winner of Philips Internal Hackathon for Healthcare Innovation using Data Analytics.
Clinical decision support for the Speech Therapist to diagnose and therapy speech problems in pediatric patients.
Implemented the SVM based classification for recorded/live voice of a pediatric patient (based on set of defined statements) and performs Pattern Recognition and Matching to identify the abnormalities in the speech waveform (against threshold values) and generates diagnostic report using PyTorch, Wavenet.
Created a predictive model based on ANN, to forecast the recovery of stroke patients, using hemodynamic parameters which function as the clinical decision support system that will help clinicians.
Heart Monitoring Prediction using IOT approach based on sensors information as input for the prediction model to give diagnosis and therapy suggestions.
Implemented and Technical guidance to the team for Hyper Spectral Image analysis for Healthcare and Biotech projects – BIoPython, Spark, and C#.