Headline

SDE I at Motorola Solutions | Data and ML | FinOps Anomaly Detection on Cloud Cost Data | Python, PySpark, Databricks, Airflow | RAG-based NLP Systems | Azure | Data Science

About

I enjoy working at the intersection of data, machine learning, and cloud systems, where models move beyond notebooks and start creating real-world impact. I’m a Machine Learning & Data Engineer with more than 2 years of industry experience, currently at Motorola Solutions, where I work on scalable data pipelines and ML-driven systems across cloud platforms. My work spans Python, PySpark, Databricks, and distributed data processing, supporting analytics, monitoring, and ML use cases at scale. A key part of my experience involves working with large-scale time-series cloud usage and cost data (FinOps)—contributing to feature engineering and reliability improvements for ML-based anomaly detection systems. I’ve also built and integrated RAG-based NLP components using internal document knowledge, enabling fast, contextual responses within internal tools and workflows. Previously, I’ve worked in fintech research and trading systems, designing data pipelines, backtesting frameworks, and signal generation models, and gained early exposure to enterprise database design during my internship at Reliance Industries. Beyond industry work, I enjoy applied ML research. My work on Spam Email Detection was presented at ICTIS 2024 and published in Springer LNNS, reflecting my interest in combining theory with production-oriented thinking. I’m a strong believer in continuous learning and have been fortunate to earn recognition through hackathons, certifications, and academic excellence, while constantly sharpening both my technical skills (ML, data engineering, cloud) and soft skills (communication, ownership, collaboration). 💡 Interests: FinTech • Data Engineering • Data Science • Machine Learning • Cloud ML Systems 📬 Open to: Data Engineering, Machine Learning, and Data Science roles where I can build scalable systems, learn from strong teams, and solve meaningful problems. Feel free to connect or reach out—always happy to discuss data, ML, or interesting problems.

Location

Bengaluru

AI stack

claude code

cursor

gemini

github copilot

mcp

openai

Work experience

SDE I at Motorola Solutions, Aug 2026 - Present · 2 mos, Bengaluru · Hybrid

SDE (data engineer) at Motorola Solutions, Jun 2025 - Aug 2026 · 1 yr 3 mos, Bengaluru · Hybrid, - Designing and orchestrating scalable data pipelines on Databricks and Apache Airflow, using Python & PySpark to streamline data processing across distributed systems. - Deploying and running Spark workloads on Kubernetes, supporting high-throughput and low-latency data workflows. - Enhanced an ML-based anomaly detection model that analyzes cloud usage and cost patterns to detect anomalies and trigger automated notifications, improving monitoring accuracy and reliability. - Improving and maintaining Azure-based data ingestion systems, including troubleshooting pipelines, enhancing performance, and integrating new data sources when needed. - Working hands-on across the Azure cloud stack (ADLS, Kubernetes, Databricks, CI/CD) for end-to-end data engineering delivery: from ingestion to transformation and consumption.

Python Developer at Neev Finance, Feb 2025 - Jul 2025 · 6 mos, San Francisco, California, United States, - Designed end-to-end data pipelines and automation for investment datasets using Python. - Developed and optimized systematic trading models and signal generation modules. - Supported research by improving backtesting frameworks and performance reports. - Collaborated with the team to apply quantitative finance techniques in live research.

Software Engineer Intern at Motorola Solutions, Jun 2024 - Jun 2025 · 1 yr 1 mo, Bengaluru · Hybrid, - Explored and integrated ComfyUI for AI-powered UI enhancements, contributing to a more intuitive and efficient user experience. - Developed RAG-based chatbots trained on internal PDF knowledge bases, enabling fast, contextual, and locally hosted support for internal tools and website integrations. - Engineered cloud-native infrastructure using Azure, Docker, Kubernetes (K8s) and HAProxy, improving scalability, fault tolerance, and deployment reliability across AI/ML workloads. - Implemented CI/CD pipelines in Azure DevOps, automating build, test, and deployment workflows for containerized services. - Performed load and performance testing using Locust, identifying bottlenecks and driving system enhancements that improved application throughput and stability.

Student Placement Coordinator at CAREER DEVELOPMENT AND PLACEMENT CELL , CHARUSAT, Mar 2024 - Jun 2024 · 4 mos, Anand, Gujarat, India

Chief Technology Officer at Charusat Learning And Development Club, Mar 2023 - Nov 2023 · 9 mos, Anand, Gujarat, India, As the CTO for AI/ML and Data Science, I have diligently expanded my expertise while leading a collaborative team in executing diverse projects. This role has allowed me to enhance my knowledge, drive innovation, and deliver impactful solutions in the field of artificial intelligence, machine learning, and data science.

Public Relations Coordinator at GDSC CHARUSAT, Aug 2022 - Jul 2023 · 1 yr, CHARUSAT UNIVERSITY

Education

Bachelor of Technology - BTech, Computer Engineering, CHARUSAT

Associate of Science - AS, Science, Kokilaben Dhirubhai Ambani Reliance Foundation School

Public links

github: https://github.com/krishna-shah-07

leetcode: https://leetcode.com/krish1311