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I am a Machine Learning Engineer with 3.5+ years of experience delivering end-to-end data and ML solutions across NLP, computer vision, and anomaly detection. I build and deploy models using TensorFlow, PyTorch, and Hugging Face, and develop APIs with FastAPI and Flask.\n\nI am proficient in cloud platforms (AWS, GCP) and MLOps, including Docker, Kubernetes, and CI/CD pipelines. I enjoy collaborating with cross-functional teams in Agile environments to translate complex data insights into impactful business solutions.…I am a Machine Learning Engineer with 3.5+ years of experience delivering end-to-end data and ML solutions across NLP, computer vision, and anomaly detection. I build and deploy models using TensorFlow, PyTorch, and Hugging Face, and develop APIs with FastAPI and Flask.\n\nI am proficient in cloud platforms (AWS, GCP) and MLOps, including Docker, Kubernetes, and CI/CD pipelines. I enjoy collaborating with cross-functional teams in Agile environments to translate complex data insights into impactful business solutions.

Konda Sumanth Krishna

Data Scientist, AI Engineer, AI Developer, +2

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I am a Machine Learning Engineer with 3.5+ years of experience delivering end-to-end data and ML solutions across NLP, computer vision, and anomaly detection. I build and deploy models using TensorFlow, PyTorch, and Hugging Face, and develop APIs with FastAPI and Flask.\n\nI am proficient in cloud platforms (AWS, GCP) and MLOps, including Docker, Kubernetes, and CI/CD pipelines. I enjoy collaborating with cross-functional teams in Agile environments to translate complex data insights into impactful business solutions.…I am a Machine Learning Engineer with 3.5+ years of experience delivering end-to-end data and ML solutions across NLP, computer vision, and anomaly detection. I build and deploy models using TensorFlow, PyTorch, and Hugging Face, and develop APIs with FastAPI and Flask.\n\nI am proficient in cloud platforms (AWS, GCP) and MLOps, including Docker, Kubernetes, and CI/CD pipelines. I enjoy collaborating with cross-functional teams in Agile environments to translate complex data insights into impactful business solutions.

Available to hire

I am a Machine Learning Engineer with 3.5+ years of experience delivering end-to-end data and ML solutions across NLP, computer vision, and anomaly detection. I build and deploy models using TensorFlow, PyTorch, and Hugging Face, and develop APIs with FastAPI and Flask.\n\nI am proficient in cloud platforms (AWS, GCP) and MLOps, including Docker, Kubernetes, and CI/CD pipelines. I enjoy collaborating with cross-functional teams in Agile environments to translate complex data insights into impactful business solutions.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Language

English

Fluent

Work Experience

Machine Learning Engineer / Analyst at Principal Financial Group

January 1, 2025 - Present

Architect and deploy production-grade neural networks using TensorFlow and Python, leveraging Distributed Training to reduce model training time by 40% on large-scale datasets while improving experiment throughput and model iteration speed for delivery timelines. Automate the ML lifecycle by building CI/CD pipelines with MLflow and Jenkins, enabling repeatable training runs, reliable model promotion across environments, and consistent tracking of experiments, artifacts, and deployments across teams. Design and implement automated Drift Detection using AWS CloudWatch and SageMaker Model Monitor, reducing manual auditing time by 30% and improving alert quality across production models. Monitor and mitigate drift and bias using Responsible AI (RAI) frameworks, enforcing fairness checks and explainability reviews to support compliant, governed model decisions. Decrease deployment latency by 60% by standardizing MLOps using MLflow and Kubernetes, improving rollout stability, scaling reliabi

Data Scientist at Bytecraft System

August 1, 2021 - December 1, 2023

Engineered robust ETL/ELT pipelines using PySpark and Databricks to efficiently process multi-terabyte datasets, enabling large-scale predictive modeling workflows and seamless data integration across enterprise systems. Developed and fine-tuned advanced Computer Vision and NLP models with PyTorch and Hugging Face Transformers, streamlining complex data extraction processes and reducing manual annotation efforts by over 40%. Improved F1-score from 0.82 to 0.94 for a fraud detection system through sophisticated feature engineering and hyperparameter optimization using Ray Tune, enhancing real-time transaction monitoring capabilities. Executed comprehensive A/B testing and statistical validation for production models, resulting in significant gains in precision, recall, and key business KPIs such as reduced false positives by 20%. Enhanced prediction accuracy by 15% for risk assessment models by building a robust RAG pipeline incorporating real-time market data from vector databases, imp