Seattle, Washington · United States

Krishnaditya Kancharla

Applied AI Architect @ Google — Strategic Accounts (AI FDE Org)

Applied AI Architecture · Strategic Enterprise Engagements

Currently working as an AI/ML lead for strategic engagements at Google. I’m thrilled to be at the forefront of AI, a technology poised to reshape our world as profoundly as bipedalism did for our ancestors.

Having 5+ years of experience working at Hyperscalers like Google Cloud and Amazon Web Services (AWS), I have architected and implemented end-to-end Machine Learning and Generative AI systems for many of the Fortune 100 companies. Driven by the belief that AI represents humanity’s next evolutionary step, I build intelligent systems that push the boundaries of what’s possible.

Explore Career Journey View Full CV Summary Contact Me
Career Trajectory

Experience & Impact

From systems engineering and DevOps to leading GenAI and Applied AI engagements across Google and Amazon Web Services.

Applied AI Architect Current

Google · Strategic Accounts (AI FDE Org)
July 2025 – Present Seattle, WA
  • Serving as AI/ML Lead for Google’s strategic enterprise engagements within the Forward Deployed Engineering (AI FDE) organization.
  • Architecting and deploying production-grade Applied AI, Generative AI, and intelligent agentic systems tailored to complex Fortune 100 workflows.
  • Partnering with executive and engineering leadership to translate frontier foundation model capabilities into measurable enterprise outcomes on Google Cloud.
Applied AI Google Cloud Generative AI Strategic Enterprise Architecture AI FDE
AWS logo

GenAI Solutions Architect & Machine Learning Engineer - II

Amazon Web Services (AWS) · Professional Services
Jan 2023 – July 2025 · 2 yrs 7 mos Seattle, Washington, United States
GenAI Solutions Architect August 2024 – July 2025 (1 year)
  • Designed and delivered enterprise-scale Generative AI architectures, LLM orchestration pipelines, and retrieval-augmented systems on AWS.
  • Guided strategic enterprise accounts from prototype evaluation through secure, high-throughput production deployment.
Machine Learning Engineer - II January 2023 – September 2024 (1 yr 9 mos)
  • Served as AI/ML Lead for the strategic enterprise segment in AWS Professional Services.
  • Implemented end-to-end Machine Learning systems, automated feature engineering, and scalable MLOps pipelines on AWS for Fortune 100 clients.
Generative AI Amazon SageMaker End-to-End MLOps Python 3 PyTorch Cloud Solution Architecture

Graduate Student Researcher

University of Illinois Urbana-Champaign
Sept 2022 – Dec 2022 · 4 mos Champaign, Illinois, United States
  • Research Focus: Machine Learning Methodologies for Social Sensing.
  • Investigated statistical learning, natural language processing, and graph/network signal modeling for reliable inference from noisy social data streams.
Social Sensing Natural Language Processing Machine Learning Research Python
AWS logo

Machine Learning Intern

Amazon Web Services (AWS) · AWS Professional Services Org
May 2022 – Aug 2022 · 4 mos Austin, Texas, United States
  • Conducted exploratory data analysis on Bank Telemarketing datasets to uncover actionable customer conversion and campaign insights.
  • Developed a scalable inference pipeline on Amazon SageMaker to power an automated recommendation engine.
  • Visualized Customer Experience (CX) metrics including CSAT and NPS by building interactive executive dashboards on Amazon QuickSight.
Amazon SageMaker Recommendation Engines Exploratory Data Analysis Amazon QuickSight
AWS logo

Cloud Engineer (Machine Learning) & DevOps Associate

Amazon Web Services (AWS) · AWS Premium Support
July 2019 – Aug 2021 · 2 yrs 2 mos Bengaluru, Karnataka, India
Cloud Engineer - Machine Learning December 2020 – August 2021 (9 mos)
  • Designed and deployed critical ML workloads on AWS and implemented end-to-end ML systems for high-scale batch predictions, significantly reducing recurring client cloud costs.
  • Developed live KPI monitoring dashboards for production ML workloads and automated ETL collection of siloed data from heterogeneous sources.
  • Led a team of 16 associates and strategized operational processes that improved SLA by 37.5%.
  • Awarded AWS Superstar of the Quarter (Q4 2020) and commended by Site Leader & Account Manager for migrating enterprise ML workloads from Azure to AWS.
  • Delivered Cross-Site Training sessions and Live Learning Labs for Cape Town (CPT) and Sydney (SYD) sites; graduated from Amazon Machine Learning University and earned 4 AWS Certifications + AWS Tier-2 Speaker Certification.
DevOps Associate July 2019 – December 2020 (1 yr 6 mos)
  • Analyzed resource metrics and log dumps for fault localization and performance optimization of mission-critical DevOps infrastructure hosted on AWS.
  • Oversaw Enterprise Escalations to identify, assess, and mitigate production impact issues across AWS application deployment services.
  • Inducted into the Core Software Development Team after completing the Amazon SDE Bootcamp; authored CloudFormation templates and Python automation programs that optimized internal application uptime by 40%.
SageMaker TensorFlow PyTorch Scikit-learn Docker & ECS CloudFormation MySQL Pandas
SPIT logo

Teaching Assistant — Applied Mathematics

Bhartiya Vidya Bhavans Sardar Patel Institute of Technology (SPIT)
July 2017 – April 2019 · 1 yr 10 mos Mumbai, India
  • Undergraduate Teaching Assistant for Applied Mathematics I, II, and III under Prof. Nida Bakereywala.
  • Conducted lectures and doubt-clearing sessions on Probability Distributions, Linear Algebra, and Vector Calculus, and curated problem sets for freshman and sophomore cohorts.
Applied Mathematics Probability & Statistics Vector Calculus
Headstrait logo

Data Science Intern

headstrait
Dec 2018 – Feb 2019 · 3 mos Mumbai, India
  • Devised supervised and unsupervised machine learning solutions for sports analytics on historical ODI cricket matches played between 2000 and 2010.
  • Designed a Convolutional Neural Network (CNN) for facial recognition of cricket players using PCA feature extraction.
  • Implemented Gaussian Mixture Model (GMM) unsupervised clustering to identify batsmen with similar batting styles to assist in league player auctions and substitutions.
  • Achieved 94.5% accuracy predicting venue-specific team match totals and estimating individual batsman run output against specific bowling attacks.
Python TensorFlow PyTorch R SQL MongoDB
IBM logo

Summer Intern

IBM
June 2017 – July 2017 · 2 mos Mumbai, India
  • Analyzed enterprise infrastructure downtime reports for proactive fault prediction and predictive maintenance.
  • Configured fault-tolerant DHCP and DNS servers using automated Bash scripts.
  • Implemented Dynamic NAT by configuring NAT routers to dynamically allocate public IPs from address pools to private network devices.
Linux Administration Bash Scripting Predictive Maintenance Network Engineering
Technical Arsenal

Areas of Expertise & Skills

Full-stack AI/ML engineering, cloud solution architecture, distributed data pipelines, and site reliability.

Core Areas of Expertise
Applied AI & Generative AI Machine Learning Natural Language Processing (NLP) Cloud Solution Architecture Feature Engineering Exploratory Data Analysis Data Manipulation DevOps & MLOps Site Reliability Engineering Linux System Administration

ML, AI & Data Science

Core Focus
Machine Learning Generative AI / LLMs Google Cloud AI / Vertex AI Amazon SageMaker PyTorch TensorFlow Scikit-learn (Sklearn) NumPy Pandas Seaborn SciPy OpenCV

Programming Languages

Polyglot
Python 3 Go (Golang) C++ C Java Bash Scripting MySQL / SQL R HTML5 & CSS3 JavaScript

Cloud & DevOps Engineering

Hyperscale
Google Cloud Platform (GCP) Amazon Web Services (AWS) Docker Kubernetes Terraform Jenkins Cloud Functions & Lambda AWS ECS & Fargate CloudFormation CodeDeploy Ansible Chef Puppet

Data Frameworks, BI & Systems

Distributed
Apache Spark Apache Kafka Apache Hadoop Tableau Power BI Amazon QuickSight MongoDB Amazon DynamoDB Flask API Apache Solr memcached HHVM Git
Research, Engineering & Writing

Publications, Projects & Articles

Peer-reviewed IEEE research, production ML systems, and technical deep-dives on cloud architecture.

Explore GitHub Repositories
IEEE Peer-Reviewed Publication DOI: 10.1109/ICACAT.2018.8933575 · ICACAT 2018

Handwritten Signature Recognition : A Convolutional Neural Network Approach

Krishnaditya Kancharla, Varun Kamble, and Mohit Kapoor · Published in IEEE International Conference on Advanced Computation and Telecommunication (ICACAT)

View on IEEE Xplore
Technical Article · Medium

Preparing for the AWS Certified DevOps Engineer — Professional Exam (DOP-C01)

Comprehensive architectural guide and preparation strategy covering CI/CD automation, infrastructure as code, high availability, and incident response on AWS.

Feb 20, 2021 Read on Medium →
Technical Article · Medium

Creating a Custom Kafka Cluster Configuration Using Macros in AWS CloudFormation

Deep dive into automating custom Apache Kafka cluster provisioning using AWS CloudFormation Macros and serverless transformations.

May 3, 2020 Read on Medium →
Cloud & AIOps System

Real-Time Application Performance Telemetry & Auto-Remediation

Built a real-time telemetry pipeline publishing performance metrics to AWS CloudWatch with automated AWS Lambda remediation triggers, improving application uptime by 40%.

Python · Boto3 · CloudWatch · DynamoDB AWS Internal
Deep Learning · Computer Vision & NLP

Encoder-Decoder Image Captioning System

Designed a neural architecture combining a CNN visual feature encoder with an LSTM sequence decoder to generate grammatically accurate natural-language descriptions of input images.

CNN · LSTM · Deep Learning GitHub →
Academic Foundation & Recognition

Education, Certifications & Honors

Education

University of Illinois Urbana-Champaign
2021 – 2022
Master of Science (MS), Data Science & Analytics

School of Information Sciences · Graduate Student Researcher in Machine Learning Methodologies for Social Sensing.

Sardar Patel Institute of Technology (SPIT)
2015 – 2019
Bachelor of Technology (B.Tech.), Electronics & Telecommunications Engineering

University of Mumbai (GPA: 7.8/10) · Coursework: Neural Networks & Fuzzy Logic, OOP in Java, Computer Networking, Image & Video Processing, Operating Systems.

Shri T.P. Bhatia Jr. College Of Science
2013 – 2015
12th Grade (HSC), Science

Mumbai, India · Scored 83.85%.

Rustomjee Cambridge International School
2012
International General Certificate of Secondary Education (IGCSE)

Awarded the Cambridge International Certificate in Education for Stellar Academic Performance (91%).

Certifications & Honors

Professional Machine Learning Engineer
Google Cloud
Certified Machine Learning Architect

Validated expertise in framing, architecting, and productionizing ML models on Google Cloud.

AWS Superstar of the Quarter
Q4 2020
Amazon Web Services Honor

Recognized for technical leadership, improving team SLA by 37.5%, and spearheading enterprise ML workload migrations to AWS.

4x AWS Certified & ML University Graduate
AWS
Solutions Architect, DevOps & AWS Tier-2 Certified Speaker

Graduated from Amazon Machine Learning University, secured 4 AWS Certifications, and certified as an AWS Tier-2 Technical Speaker.

Academic & Engineering Distinctions
2016 – 2019
IIT Madras Elite & Gold · Departmental Project Awards

Ranked in the Top 5% nationwide in IIT Madras’s “Data Structures & Algorithms using Python”; 1st Prize (5th Sem) & 2nd Prize (6th Sem) Departmental Engineering Project winner; Mathematics Teacher volunteer at Abhyudaya.

Perspective

Quotes to Live By

View My Calibrated Bucket List →

“Your time is limited, so don't waste it living someone else's life. Don't be trapped by dogma – which is living with the results of other people's thinking.”

— Steve Jobs

“A man must be big enough to admit his mistakes, smart enough to profit from them, and strong enough to correct them.”

— John C. Maxwell

“Per Ardua Ad Astra — Through Adversity To The Stars.”