Professional Overview
Overall 20+ years of experience across various technological platforms as Data & Cloud Architect in the fields of Marketing, Gaming, Banking & Insurance.Mid-Level Startups 9+ years of Experience in Data & Cloud architectures: Hands-on experience in designing, building, optimizing scalable data pipelines, data processing, data management, data governance, DB design, data modeling, data cataloging etc. Worked in mid Level startup companies ‘Machine Zone’ & ‘Exabeam’ with cutting edge data and cloud technologies.Built Massive Big data pipelines & handled Successfully Multi-Terabyte Data Sets using tech like Apache Spark, PySpark, Hive, Kafka, DeltaLake, Supersets, Airflow etc & ElasticSearch- Datasets Acquisition, Processing, Cleaning, Data wrangling, Visualizations, Analytics Management, deployments.Extensive experience in Databases, Data Warehouses, MDM, NoSQL Databases, Delta Lake.Extensive experience in AWS environment (EC2, S3, Lambda, Glue, EKS, Kinesis, CloudFront, DynamoDB, RedShift, SageMaker etc)Extensive experience in Oracle OCI, AWS, GCP cloud environmentsDemonstrated success in reducing data processing time and improving data quality, discovery, accessibility for analytics teams working seamlessly in cross-functional teams.Kubernetes Orchestration, Dev & Testing Experience: Proficient in designing, deploying, and managing containerized applications, workflows using Kubernetes and micro services architecture working with Operators, Schedulers, Controllers, CRDs. Experienced in automating deployments, managing stateful and stateless applications, and leveraging Kubernetes-native tools like Fluentd, Istio Service Mesh, Prometheus monitoring, Ceph DB, Linkerd etc.DevOps & CI/CD Pipelines: Proven track record in implementing CI/CD practices that align with cloud-native development. Worked with tools like Jenkins, GitLab CI/CD, TeamCity builds, Nexus Repos to automate build, test, and deployment processes seamlessly within Kubernetes environments.Experienced in ML Modeling, Feature Engineering (Extracting, Transforming, selecting features), algorithms Classification & Regression.ML pipelines (OCI ML, Spark Engine, Databricks MLFlow, PyTorch)LLMs, Model Deployment, Training, Inference, Monitoring. A/B Experiments Tracking, Management & Analytics
Areas of Expertise
NLP & Prompt Engineering
Build natural language processing systems, fine-tune large language models, and craft reliable production prompts for AI-powered products.
AI & Machine Learning Engineers
Design, train, and ship production ML models — from data pipelines to model inference at scale. Covers supervised learning, neural networks, LLM integration, and model optimisation.
MLOps & AI Infrastructure
Architect scalable ML infrastructure — CI/CD for ML, model versioning, experiment tracking, monitoring, and cloud deployment on AWS, GCP, or Azure.
