Vikki Jakkulwar - AI/ML Engineer

Vikki Jakkulwar

AI/ML Engineer & Data Scientist

10+ Years Experience • Azure ML • MLOps • Generative AI
● Senior AI/ML Talent
Experience 10+ Years
Current Role AI Engineer / Data Scientist
Core Focus Azure AI / MLOps / GenAI
Profile AI/ML Engineer
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Professional Summary

  • Dynamic and results-oriented AI/ML Engineer and Data Scientist with 10+ years of experience delivering scalable, intelligent, and data-driven solutions across telecommunications, engineering, and manufacturing domains.
  • Specialized in Machine Learning, Deep Learning, Natural Language Processing (NLP), and Generative AI technologies for business transformation, predictive analytics, and operational excellence.
  • Strong expertise in designing, developing, and deploying end-to-end AI/ML solutions using Python, PyTorch, spaCy, Pandas, NumPy, and Scikit-learn.
  • Extensive experience with the Microsoft Azure AI and ML ecosystem, including Azure Machine Learning Studio, Azure OpenAI, Azure AI Search, Azure Cognitive Search, Azure Document Intelligence, Azure Blob Storage, ADF, AKS, and Azure Data Lake.
  • Proven expertise in scalable ML pipelines covering data ingestion, feature engineering, model training, validation, inference, deployment, monitoring, and automation.
  • Hands-on experience implementing MLOps frameworks using MLflow for experiment tracking, model registry, versioning, logging, monitoring, and model lifecycle governance.
  • Experienced in building Retrieval-Augmented Generation (RAG) architectures, embedding models, vector databases, semantic search, and cognitive AI solutions.
  • Strong understanding of cloud-native AI deployment and containerization with practical experience deploying ML, AI, and GenAI workloads on Azure Kubernetes Service.
  • Skilled in implementing CI/CD pipelines using Git and Azure DevOps for automated model deployment, testing, integration, monitoring, and continuous delivery.
  • Proficient in ETL workflows, big-data processing, statistical modeling, A/B testing, predictive analytics, and data visualization.
  • Strong knowledge of AI governance, compliance, responsible AI practices, and model fine-tuning methodologies.
  • Experienced in collaborating with Data Scientists, Data Engineers, DevOps teams, business stakeholders, and business leaders to architect AI-driven solutions.
  • Seeking challenging AI/ML or Azure ML opportunities focused on Machine Learning, MLOps, Azure AI Services, and Generative AI.

Work History

Jet2.com and Jet2holidays

AI Engineer / Data Scientist
Nov 2025 – Present
  • Designing, developing, and deploying scalable Machine Learning, AI, and Generative AI solutions using Azure Machine Learning Studio, AKS, Azure Blob Storage, and Azure Data Factory.
  • Building end-to-end ML pipelines covering data ingestion, preprocessing, feature engineering, model training, validation, deployment, and monitoring.
  • Implementing automated CI/CD workflows using Git repositories and Azure DevOps Pipelines across multiple environments.
  • Developing and optimizing Python-based ML applications, reusable scripts, and automation frameworks.
  • Managing MLflow experiment tracking, model registry, artifact logging, model versioning, and performance monitoring.
  • Deploying real-time and batch inference solutions on Azure Kubernetes Service for scalable and high-availability ML workloads.
  • Creating automated data orchestration workflows using Azure Data Factory for structured and unstructured enterprise data.
  • Managing Azure Blob Storage for datasets, training artifacts, model outputs, and pipeline logs.
  • Implementing MLOps practices covering automated retraining, monitoring, rollback strategies, and deployment governance.
  • Monitoring model performance, drift detection, prediction accuracy, and infrastructure utilization.
  • Collaborating with Data Scientists, Data Engineers, DevOps teams, and business stakeholders on scalable ML architectures.
  • Developing containerized ML solutions using Docker and deploying them on AKS clusters.
  • Automating retraining and deployment using Azure ML schedules, Azure DevOps release pipelines, and event-driven triggers.
  • Configuring secure access controls, role-based access, secrets management, and compliance policies within Azure.
  • Integrating REST APIs and external enterprise applications with Azure ML services for AI-driven process automation.
  • Creating reusable ML components, templates, and deployment frameworks for standardized ML development.
  • Supporting infrastructure provisioning and environment configuration using Infrastructure as Code and Azure-native services.
  • Troubleshooting deployment issues, pipeline failures, model-serving errors, and infrastructure bottlenecks.
  • Preparing technical documentation, architecture diagrams, deployment procedures, and operational runbooks.
  • Continuously evaluating Azure AI, MLOps, and Generative AI capabilities to improve scalability and model performance.

Michelin

AI Engineer / Data Scientist
Aug 2022 – Nov 2025
  • Developed and orchestrated end-to-end ETL pipelines using Python, Azure Data Factory, and Azure Blob Storage.
  • Ingested, cleansed, transformed, and loaded structured and unstructured datasets into Azure Data Lakes.
  • Designed scalable machine learning workflows using Azure Machine Learning Studio.
  • Enabled automated model training, validation, deployment, and lifecycle management across cloud-native environments.
  • Leveraged Azure OpenAI, Azure Cognitive Search, Azure AI Search, and Document Intelligence for intelligent AI and GenAI applications.
  • Built solutions for semantic search, document extraction, and contextual data processing.
  • Engineered and deployed containerized ML solutions on AKS for high availability and scalable inference.
  • Used Pandas, NumPy, spaCy, Scikit-learn, and PyTorch for NLP, feature engineering, deep learning, and predictive modeling.
  • Implemented MLflow for experiment tracking, model versioning, artifact management, model registry, and monitoring.
  • Built CI/CD pipelines using Azure DevOps, Git, and YAML-based workflows.
  • Applied MLOps practices including automated retraining, model monitoring, drift detection, and continuous performance evaluation.
  • Designed monitoring frameworks for prediction accuracy, data drift, latency, and resource utilization.
  • Implemented metadata tracking, feature-store integration, and data-lineage mechanisms for ML governance.
  • Built multimodal AI solutions combining Computer Vision and NLP to classify European Article Numbers using product images and textual metadata.
  • Integrated OpenAI LLMs to extract contextual insights, semantic relationships, and enriched product information.
  • Applied multimodal fusion strategies including attention-based architectures and late-fusion methods.
  • Developed forecasting solutions using ARIMA, Exponential Smoothing, Random Forest, Gradient Boosting, XGBoost, and LSTM.
  • Evaluated predictive models using MAE, RMSE, MSE, R² Score, and bucket-based validation methodologies.
  • Designed ML-based optimization solutions for mining operations using fuel consumption, payload efficiency, haul distance, terrain, telemetry, and GPS data.
  • Engineered advanced features from haul-cycle data, altitude metrics, payload distribution, and gradient analysis.
  • Developed optimization recommendations that reduced fuel consumption and improved fleet productivity.
  • Created interactive dashboards using Plotly and Matplotlib for operational KPIs, forecasting insights, and ML recommendations.
  • Automated batch and real-time inference pipelines using Azure ML endpoints and AKS deployments.
  • Managed Docker, AKS, and Azure cloud-native deployment environments.
  • Implemented secure authentication, access management, and role-based security controls.
  • Optimized Azure resource utilization and ML infrastructure performance using monitoring and scaling policies.
  • Participated in architectural discussions and technical design reviews for AI, ML, and GenAI platforms.
  • Documented ML workflows, deployment procedures, model evaluation reports, and operational runbooks.

L&T Technology Services (LTTS)

AI Engineer / Data Scientist
Jan 2021 – Aug 2022
  • Designed scalable data architectures including data lakes, data warehouses, and streaming platforms.
  • Developed and maintained data models, schemas, and dictionaries aligned with enterprise governance standards.
  • Built automated data pipelines using Python, Pandas, and NumPy for sensor-to-analytics data flows.
  • Leveraged PyTorch for deep learning development, experimentation, and performance optimization.
  • Used Azure Data Lake for centralized storage, retrieval, and ML model training.
  • Integrated ETL processes to prepare structured and unstructured data for AI and ML workflows.
  • Applied statistical analysis and feature-importance techniques to identify parameters affecting EV battery degradation.
  • Developed automation scripts for model retraining and version control.
  • Implemented AI model monitoring frameworks for model drift, data quality, and performance.
  • Collaborated with data engineering teams to design CI/CD pipelines for AI model deployment.
  • Maintained transparent documentation, reproducible workflows, and ethical data usage practices.
  • Processed embedded EV sensor data including voltage, current, temperature, and charging/discharging cycles.
  • Engineered predictive features including State of Charge, State of Health, and vehicle usage patterns.
  • Designed predictive models using Regression, Random Forest, and LSTM networks.
  • Deployed predictive analytics models for real-time EV battery-health monitoring and early degradation detection.
  • Conducted post-deployment evaluations to validate predictive accuracy under real-world conditions.
  • Presented analytical findings and model insights through interactive dashboards and visualizations.

Vodafone

Data Scientist
May 2015 – Jan 2021
  • Orchestrated the “Data First” initiative by integrating machine learning models into enterprise decision-making.
  • Applied Random Forest, Gradient Boosting, and Neural Networks for enterprise analytics.
  • Designed ETL pipelines and automated data-cleansing workflows.
  • Built predictive analytics models for customer behavior and churn forecasting.
  • Used Logistic Regression, Random Forest, XGBoost, and Voting Classifiers to improve model precision.
  • Conducted customer segmentation and conversion-funnel analysis for targeted marketing strategies.
  • Led NLP-based sentiment-analysis projects using customer feedback from multiple channels.
  • Applied Naïve Bayes and Support Vector Machine models for sentiment classification and topic modeling.
  • Developed RESTful APIs to integrate ML-driven insights into dashboards.
  • Automated reporting and real-time decision support through API-based ML integration.
  • Collaborated with analytics, marketing, product, and cross-functional teams.
  • Used containerization and API-based deployment frameworks to improve ML model scalability and reusability.
  • Validated model performance using cross-validation, ROC-AUC analysis, and benchmarking.
  • Documented workflows, model designs, and data lineage to support governance and audit readiness.

Skillsets

Machine Learning Deep Learning Natural Language Processing Generative AI Large Language Models RAG MLOps MLflow Predictive Analytics Forecasting Computer Vision Statistical Modeling A/B Testing Model Fine-Tuning AI Governance Responsible AI Azure Machine Learning Azure OpenAI Azure AI Search Azure Cognitive Search Azure Document Intelligence Azure Data Factory Azure Data Lake Azure Blob Storage AKS Azure DevOps Python PyTorch TensorFlow Scikit-learn Pandas NumPy SciPy PySpark NLTK spaCy Keras OpenCV Docker Git CI/CD

Applications & Tools

Azure AI & Cloud
Azure Machine Learning Azure OpenAI Azure AI Search Azure Cognitive Search Document Intelligence Azure Blob Storage Azure Data Factory Azure Data Lake Azure Kubernetes Service Azure DevOps
Programming & ML
Python Pandas NumPy SciPy Scikit-learn PyTorch TensorFlow Keras spaCy NLTK OpenCV Beautiful Soup Geopy
Data & Databases
PostgreSQL MongoDB Cassandra Azure Data Lake AWS S3 Spark Hadoop
Visualization
Matplotlib Plotly Seaborn Power BI
Machine Learning
Regression Classification Forecasting NLP Computer Vision LLMs Optimization Random Forest XGBoost LSTM ARIMA
DevOps & MLOps
MLflow Git Azure DevOps Docker CI/CD YAML Pipelines MLOps Model Registry Model Monitoring

Certifications

  • Azure Data Scientist Associate
  • AWS Certified Machine Learning – Specialty
  • TensorFlow Developer Certificate

Notable Achievements

  • Designed and deployed end-to-end AI/ML and Generative AI solutions across telecommunications, engineering, and manufacturing domains.
  • Built production-grade Azure ML pipelines covering ingestion, preprocessing, feature engineering, training, validation, inference, deployment, and monitoring.
  • Implemented enterprise MLOps frameworks using MLflow for experiment tracking, model registry, versioning, monitoring, and governance.
  • Developed RAG, semantic-search, embedding, and cognitive-AI solutions for enterprise knowledge management.
  • Built multimodal AI solutions combining Computer Vision and NLP for product classification using image and textual data.
  • Developed forecasting solutions using ARIMA, Exponential Smoothing, Random Forest, Gradient Boosting, XGBoost, and LSTM.
  • Designed ML optimization solutions for mining operations that generated recommendations for fuel efficiency and fleet productivity.
  • Developed predictive models for EV battery health using sensor data, Regression, Random Forest, and LSTM.
  • Built customer churn, segmentation, sentiment analysis, and retention models for telecommunications use cases.
  • Developed RESTful APIs that integrated machine-learning insights into enterprise dashboards and decision-support systems.
  • Automated CI/CD and model deployment workflows using Git, Azure DevOps, YAML pipelines, Docker, and AKS.
  • Implemented model monitoring covering prediction accuracy, drift, latency, infrastructure utilization, and data quality.

Education

Master of Business Administration (MBA) – Project Management
Education details as listed in the supplied resume

Bachelor of Engineering (B.E.) – Electronics
2014
Candidate profile presented by Ace Technologies • AI / ML • Azure • MLOps • Generative AI