Generative AI - Leveraging LLM's, Open AI, HugginFace for Finetuning the models i.e.GPT3, GPT3.5, GPT4, Llama2 70B Transfer Learning and Inference Mechanism with Prompt Engineering.Synthetic Data Generation -Generate Tabular Categorical and Numerical Data using GAN (Generative Adversarial Network) and statistical Methods for Optimization.Predictive Modelling: Demand Forecasting, Clustering, Aggregating.Highlights:• Spearheaded the creation of AI-driven applications in areas such as Configuration Script Development, Document Summarization, and Project Classification.• Achieved a 10% reduction in warehouse operational expenses by designing forecasting models utilizing Databricks, Python, Prophet, ARIMA, and Croston methodologies.• Collaborated closely with clients and interdisciplinary teams to strategize and offer solutions for overcoming obstacles.• Implemented generative AI technologies, resulting in a notable 25% enhancement in response efficiency.• Employed OpenAI's GPT models to improve generative AI applications, leading to a 30% improvement in both response times and customer engagement.• Focused expertise in Data Mining, Text Analytics, and Exploratory Data Analysis (EDA).Achievements:• Contributed to an annual saving of $500K by refining warehouse management with precise demand and supply forecasting models. • Pioneered the use of generative AI in automated content generation, slashing content production expenses by 50% while upholding quality and uniformity.

Highlights:• Developed a machine learning model for Sales Transference, cutting customer attrition by 40% and boosting annual revenue by approximately $500K.• Enhanced the performance of the existing recommender engine, yielding a 10% rise in sales.• Engaged with key stakeholders to identify potential services for market basket analysis.• Utilized GCP BigQuery to increase data processing efficiency by 25%, enhancing speed in predictive modelling and data analysis.• Refined chatbot training phrases and intents, achieving over 80% accuracy.Achievements:• Implemented a chatbot on Google App Engine with auto-scaling server instances, saving over $100K annually.• Earned an on-the-spot award for exceptional support to diverse customers across various time zones.Tech Stack:DOMO, Python, Jupyter Lab, GCP - Vertex AI, Big Query, Teradata, SQL

Highlights:• Directed a team of 6 data analysts to enhance data accuracy, reducing reporting errors by 25%.• Crafted Performance Dashboards using visualization tools and predictive modelling, leading to a 20% improvement in CDQ (Cost, Delivery, Quality).• Successfully managed and delivered over 15 projects on schedule through effective resource allocation.• NLP Data Segregation from the machine outputs.

• Focused on designing effective dashboards using PowerBI and Tableau.• Consistently delivered high-priority and critical dashboards within stringent deadlines for new feature implementations.

As a Trainee engineer in Bosch ltd(NEEM), I managed the following,• Learned FMEA (Failure Mode and Effects Analysis) through IQ-RM Pro.• Inventory Management through SAP & the Lean standard of storing the stock.• Oversee supply chain activities.• Exports and Import documentation for regular shipments.• Dashboards maintenance for the Logistic & Buyer activities.• Creating proper storage mechanism for the fast-moving & Slow-moving inventories by proper bin and rack methods.

1. Great experience in the small-scale industry and my first work experience.2. Was responsible for Designing Pad printing machines (modeling and drafting) & Maintained some of the storage and managing the ledger of inventories.3. Read and interpreted blueprints, technical drawings, schematics, and computer-generated reports.