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Sudeep M K

AI Developer • Bengaluru, India • s***********@gmail.com • +91*******975 • linkedin.com/••••• • github.com/•••••

Professional Summary

MLOps Engineer with 2+ years of experience building and validating ML-driven telemetry and computer-vision pipelines, ensuring robust model inference, data quality, and production reliability across IoT and wearable domains.

Technical Skills

Programming Language: Python,Java,C++,SQL,BASIC
Cloud Platforms: Amazon Web Services
Version Control & Development Tools: Linux,Git
DevOps & Infrastructure: Docker,Continuous Integration,Continuous Deployment
API & Integrations: RESTful APIs,Postman
Messaging & Monitoring: Splunk,Grafana
Data Analysis & Visualization: Pandas,NumPy,Data Preprocessing
Machine Learning & AI: Computer Vision,Deep Learning,Object Detection,Predictive Modeling,Model Validation
AI/ML Frameworks & Libraries: PyTorch,OpenCV,scikit-learn,Hugging Face,YOLOv8
Project Management & Collaboration: RCA,Agile
Quality Assurance & Compliance: Root Cause Analysis

Work Experience

Titan Company Limited
Bengaluru, India
AI developer
Sep 2025 – Present
Worked on AI capabilities for consumer devices and wearable telemetry, validating predictive models and maintaining real-time biometric data pipelines.
Tech Stack: Python, PyTorch, Docker, REST APIs, Grafana, Pandas
  • Evaluated and validated predictive telemetry algorithms and health-insight engines used by wearable features, improving model inference consistency through systematic validation with PyTorch and Python.
  • Analyzed real-time biometric data pipelines (heart rate, readiness, sleep scores) to optimize data validation routines and reduce end-to-end pipeline latency using Pandas and Docker-based test harnesses.
  • Automated recurring root-cause analysis (RCA) workflows for production data edge cases, building repeatable diagnostic scripts that reduced manual triage time.
  • Collaborated with cross-functional ML engineering and product teams to refine trend-detection models and define model-monitoring KPIs reported via Grafana dashboards.
  • Authored production validation suites for REST API endpoints that serve model outputs; integrated checks into CI/CD flows to prevent regressions at deployment.
  • Led post-deployment investigations on inference drift and implemented rollback and feature-flagging procedures to preserve user experience while model fixes were applied.
Infiquity Auto Tech Pvt Ltd
Bengaluru, India
Associate Software Engineer – ML Operations & QA
Sep 2024 – Mar 2025
Worked on IoT and telematics analytics platforms, validating data ingestion and API integrations for fleet and vehicle telemetry solutions.
Tech Stack: Splunk, Postman, REST APIs, Git, CI, CD, Linux
  • Validated API integrations and data ingestion pipelines for telematics platforms, designing verification suites that prevented critical runtime errors prior to production deployment.
  • Executed log analysis and performance debugging using Splunk-style analytics to surface bottlenecks; optimized backend execution speed and pipeline throughput by 25%.
  • Designed structured verification test suites for REST API endpoints serving ML-driven SaaS features, resulting in a 30% reduction in operational errors.
  • Participated in Agile iterations to triage model edge cases and track software defects, coordinating reproducible test cases between QA and ML teams.
  • Implemented automated test scripts and integration checks using Postman and CI/CD hooks to increase regression coverage across releases.
  • Documented incident findings and contributed to runbooks that improved incident response time and reproducibility for production telematics issues.
Pregrad
Bengaluru, India
Software Engineer Intern
Apr 2024 – May 2024
Built and tested cross-platform mobile interface features integrated with backend ML models and RESTful microservices.
Tech Stack: Python, REST APIs, Git
  • Engineered cross-platform UI features to surface ML model outputs, ensuring stable integration with backend microservices and REST APIs.
  • Assisted in API integration testing and created structured documentation to support model handoff and deployment processes.
  • Implemented client-side validation and error handling to improve robustness of model-driven UI flows during intermittent network conditions.
  • Developed unit and integration test cases for frontend components to reduce regressions when backend contracts changed.
  • Collaborated with backend engineers to create reproducible test data and stubs for ML inference endpoints during development.
  • Maintained version control and participated in sprint planning and code reviews to deliver production-ready interface enhancements.

Projects

Object Detection Pipeline (YOLOv8)
Tools Used: YOLOv8, PyTorch, OpenCV, Python
  • Developed and validated a YOLOv8-based computer-vision detection pipeline to detect domain-specific objects and edge cases.
  • Benchmarked model performance and tuning to achieve 85% detection accuracy on validation datasets while documenting failure modes and corrective preprocessing steps.
4G Telematics Predictive Pipeline Validation
Tools Used: Python, Pandas, NumPy, REST APIs
  • Optimized high-throughput backend pipelines for telematics ingestion; diagnosed computational bottlenecks and improved processing speed by 25% across REST API-backed flows.
  • Built validation checks and monitoring hooks to catch malformed telemetry and reduce downstream model errors.
SaaS Monitoring Analytics Dashboard
Tools Used: Splunk, Grafana, Python, REST APIs
  • Implemented data pipeline validation and logging routines for a SaaS monitoring dashboard used by 50+ users.
  • Introduced RCA updates and alerting that reduced data ingestion errors by 30% and improved observability for model and pipeline issues.

Education

New Horizon College of Engineering
B.E. in Electrical and Electronics Engineering • Bengaluru, India • 2020 – 2024

Certifications

Java Programming
Web Development
UI/UX Design

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