Profile Summary
Results-driven Software Engineer with 3 years of expertise in Python backend development, AWS Cloud Services, DevOps automation, CI/CD pipelines, MLOps infrastructure, and AI/ML engineering. Specialized in machine learning model deployment, deep learning frameworks (PyTorch, TensorFlow), Infrastructure as Code (Terraform, CloudFormation), and scalable microservices architecture. Expert in MLOps lifecycle management, model monitoring, experiment tracking, and production-grade AI/ML solutions on AWS SageMaker. Proven track record of accelerating deployment cycles by 45%, reducing ML model inference time by 35%, and optimizing cloud infrastructure costs by 20%. Experienced in building enterprise-grade Python applications, implementing comprehensive monitoring solutions, and delivering end-to-end MLOps workflows that drive business value and operational excellence.
Technical Skills
Python Backend & APIs
Cloud & AWS
DevOps & CI/CD
AI/ML & MLOps
Databases & Data Engineering
Security & Testing
Work Experience
Software Developer
Broadway Infotech
Nov 2025 - Present
Develop and maintain scalable, cloud-native backend systems using Python and AWS, improving system performance, automation, and reliability by over 35% across 20+ microservices.
Design and implement secure, high-performance RESTful APIs with FastAPI and SQLAlchemy, ensuring efficient data processing and reducing latency by 20% for 500K+ daily requests.
Deploy and manage containerized microservices on AWS with Lambda, API Gateway, RDS, and EKS, achieving 99.9% uptime and robust high availability serving 100K+ users.
Optimize database performance through advanced query optimization and schema tuning, enhancing response times by up to 40% for PostgreSQL databases handling 50GB+ data.
Automate CI/CD pipelines using Jenkins and Docker, increasing deployment speed by 40% and reducing release errors by 20% across 15+ production services.
Manage cloud infrastructure with Terraform to enable consistent, version-controlled provisioning, cutting environment setup time by 50% for 10+ AWS environments.
Implement end-to-end observability using Prometheus, Grafana, and CloudWatch to proactively monitor system health and improve resource utilization by 25%.
Collaborate with DevOps teams to maintain production-grade environments, decreasing downtime by 25% and boosting system stability across multi-region deployments.
Software Engineer
NextCS Optima
May 2025 - Oct 2025
Engineered and maintained scalable, cloud-native backend and machine learning systems on AWS, ensuring 99.9% high availability and reliability for 200K+ daily active users.
Developed and optimized backend APIs and microservices in Python and FastAPI, enhancing system performance by 30% and handling 1M+ API requests daily.
Deployed and managed serverless applications with AWS Lambda and containerized services on ECS; orchestrated workloads using Kubernetes, boosting operational efficiency by 35% across 25+ services.
Streamlined infrastructure provisioning and deployment pipelines with Terraform and Jenkins, reducing manual deployment time by 40% for 8+ development environments.
Designed and implemented CI/CD pipelines, accelerating release cycles and increasing deployment frequency by 25% with zero-downtime deployments.
Supported and optimized Docker and Kubernetes containerized workloads, achieving 99.9% application uptime and improved scalability for 50+ microservices.
Implemented comprehensive monitoring, centralized logging, and proactive alerting systems using CloudWatch and ELK stack to maintain production stability across 5+ AWS regions.
Assisted in end-to-end deployment of machine learning workflows, including scalable model inference and seamless data pipeline integration processing 5TB+ daily data.
Worked closely with cross-functional Agile teams to ensure on-time delivery of high-quality software products and continuous improvement across 3+ product lines.
Integrated machine learning models into Python backend services with FastAPI, Docker, and AWS SageMaker and Lambda, enabling scalable real-time inference for 10K+ predictions per minute.
AVOC Associate (Associate ML IT-Data)
Amazon
Aug 2021 - Sep 2023
Supported production-scale machine learning systems by integrating Machine Learning, Data Engineering, and IT Operations to ensure high availability and scalability.
Managed end-to-end ML lifecycle processes, including data ingestion, preprocessing, model deployment, and real-time monitoring, improving deployment efficiency by 20%.
Collaborated with Data Scientists to operationalize machine learning models, enhancing deployment robustness and reducing time-to-market by 15%.
Implemented comprehensive data quality and validation frameworks, increasing data accuracy by approximately 25%.
Deployed scalable ML models on AWS SageMaker and AWS Lambda, reducing model inference latency by up to 30%, resulting in faster prediction responses.
Developed scalable ETL pipelines using Apache Spark, Kafka, and AWS Glue, processing over 15TB of data daily with 99.9% reliability.
Supported NLP applications such as text classification and sentiment analysis, improving model accuracy by 18% and reducing response times by 22% for 10M+ daily requests.
Enhanced system reliability and uptime through proactive monitoring, automation, and continuous process improvements, achieving 99.9% system availability across 50+ microservices.
Built real-time streaming data pipelines with Kafka and Apache Flink, enabling efficient data processing with latency under 100 milliseconds for 1M+ events per second.
Implemented data validation pipelines using Great Expectations, increasing data consistency and reducing data errors by 30% across 200+ data workflows.
Enhanced model performance monitoring and automated retraining workflows leveraging Prometheus, MLflow, and Grafana dashboards, improving model lifecycle management efficiency by 25%.
Contributed to NLP and machine learning project implementations under senior guidance, accelerating project delivery timelines by 10%.
Academic Projects
Intelligent Stock Market Prediction System using Deep Learning
M.Tech Thesis Project - Galgotias University
Jul 2024 - Dec 2024
Developed an end-to-end machine learning system combining LSTM, GRU, and Transformer models for multi-timeframe stock market prediction with 87% accuracy
Implemented real-time data ingestion pipeline using Apache Kafka and AWS Kinesis, processing 500K+ market data points per minute
Built scalable MLOps infrastructure using Docker, Kubernetes, and MLflow for automated model training, versioning, and deployment on AWS EKS
Created comprehensive feature engineering pipeline incorporating 25+ technical indicators, sentiment analysis from 10K+ news articles daily, and market volatility metrics
Deployed production-ready FastAPI microservices with Redis caching, achieving sub-200ms response times for real-time predictions serving 1000+ concurrent users
Implemented automated backtesting framework with walk-forward validation across 5+ years of historical data, optimizing hyperparameters using Optuna
Integrated comprehensive monitoring using Prometheus and Grafana, tracking model performance drift and system metrics across distributed infrastructure
Technologies: Python, PyTorch, TensorFlow, FastAPI, Docker, Kubernetes, AWS (EKS, SageMaker, S3, RDS), Apache Kafka, Redis, MLflow, Prometheus, Grafana
Education
M.Tech | Computer Science & Engineering
Galgotias University, Greater Noida
2023-2025
B.Tech B.E. | Computer Science & Engineering
KIIT University, Bhubaneshwar
2016-2020
Research Publications
A Time-Series Forecasting Framework for Stock Market Prediction Using LSTM and Technical Indicators
CISES 2025 Conference (Offsite)
Dec 2023 - Feb 2025
Developed a comprehensive time-series forecasting framework combining LSTM neural networks with technical indicators for enhanced stock market prediction accuracy
Implemented advanced deep learning architectures using PyTorch and TensorFlow for financial time series analysis and pattern recognition
Integrated multiple technical indicators (RSI, MACD, Bollinger Bands) with LSTM models to improve prediction performance by 25%
Conducted extensive backtesting and validation using historical market data spanning 5+ years across multiple stock indices
Applied MLOps best practices for model versioning, experiment tracking, and automated retraining pipelines
Utilized AWS SageMaker for scalable model training and deployment, reducing computational costs by 30%
Keywords: Stock Market Prediction, LSTM, Machine Learning, Time-Series Forecasting, Technical Indicators, Deep Learning, Financial Market Analysis, MLOps, AWS SageMaker
Hybrid Machine Learning Framework for Stock Market Forecasting: Integrating Technical Indicators
CISES 2025 Conference (Offsite)
Sep 2023 - Jun 2025
Designed and implemented a hybrid ensemble machine learning framework combining Random Forest, XGBoost, and LightGBM for robust stock market forecasting
Engineered comprehensive feature extraction pipeline incorporating 15+ technical indicators and market sentiment analysis
Developed automated hyperparameter optimization using Optuna and Bayesian optimization techniques, improving model accuracy by 18%
Implemented real-time data ingestion and processing pipeline using Apache Kafka and AWS Kinesis for live market data
Built scalable MLOps infrastructure with Docker containerization and Kubernetes orchestration for model deployment
Created comprehensive model monitoring and drift detection system using Evidently AI and custom metrics
Deployed production-ready API endpoints using FastAPI and AWS Lambda for real-time prediction serving
Keywords: Stock Market Prediction, Technical Indicators, Machine Learning, Ensemble Models, Random Forest, XGBoost, LightGBM, Financial Time Series, MLOps, Real-time Processing
Certifications
Microsoft Python Technology Association (2023)
AWS Academy Graduate - AWS Academy Cloud Foundations (2024)
Databricks Accredited Generative AI Fundamentals (Valid until April 2027)
Docker Foundations Professional Certificate (2024)
Additional Information
Languages: Hindi, English