Pallav Anand - Python Developer | AWS Cloud Engineer | DevOps Engineer | MLOps Engineer

Core Technical Skills

Python, FastAPI, Django, Flask, REST API, GraphQL, Microservices Architecture, Backend Development

AWS Cloud: Lambda, ECS, EKS, SageMaker, S3, EC2, RDS, DynamoDB, CloudFormation, Step Functions, API Gateway, Glue, Bedrock

DevOps: Docker, Kubernetes, Terraform, Jenkins, CI/CD Pipeline, GitHub Actions, GitLab CI, Blue-Green Deployment, Rolling Deployment, Helm Charts

MLOps: Machine Learning Operations, Model Deployment, MLflow, Model Versioning, Automated Retraining, A/B Testing

Databases: PostgreSQL, MySQL, MongoDB, Redis, DynamoDB, SQLAlchemy, Database Optimization

Data Engineering: Apache Kafka, Apache Spark, Apache Flink, ETL Pipelines, Data Streaming, Real-time Processing

Machine Learning: BERT, LSTM, CNN, RNN, PyTorch, TensorFlow, Scikit-learn, NLP, Deep Learning, Neural Networks

Monitoring: Prometheus, Grafana, CloudWatch, ELK Stack, Logging, Alerting, Observability

Version Control: Git, GitHub, GitLab, Bitbucket, Code Review, Pull Requests

Methodologies: Agile, Scrum, Kanban, Test-Driven Development, Continuous Integration, Continuous Deployment

Professional Experience

3+ years of professional software engineering experience

Broadway Infotech - Software Developer (Nov 2025 - Present)

NextCS Optima - Software Engineer (May 2025 - Oct 2025)

Amazon - AVOC Associate ML IT-Data (Aug 2021 - Sep 2023)

Key Achievements

45% faster model deployment, 35% reduced inference latency, 20% cloud cost optimization

99.9% system uptime SLA, 3TB+ daily data processing

Automated CI/CD pipelines reducing deployment time by 40%

Optimized database performance improving response times by 30%

Education

Master of Technology (M.Tech) - Computer Science - Galgotias University (2023-2025)

Bachelor of Technology (B.Tech) - Computer Science - KIIT University (2016-2020)

Certifications

AWS Academy Cloud Foundations - Amazon Web Services

Generative AI Fundamentals - Databricks

Docker Foundations Professional Certificate - Docker

Python Technology Association - Microsoft

Available for Opportunities

Pallav Anand

Python Developer & AWS Cloud Engineer with 3+ years building production-grade systems using FastAPI, Django, Flask. Expert in AWS (Lambda, ECS, EKS, SageMaker), Kubernetes, Docker, Terraform, Jenkins CI/CD pipelines, and MLOps. Specialized in microservices architecture, REST APIs, PostgreSQL, Apache Kafka, and real-time data processing. Proven track record: 45% faster model deployment, 35% reduced inference time, 20% lower cloud costs.

3 Yrs Exp
99.9% Uptime SLA
3TB+ Data/Day
4 Certs
✉ Hire Me ↗ LinkedIn ⌥ GitHub
pallav@aws-prod:~
$whoami
pallav_anand // software_developer

$cat skills.json | jq .core
["Python", "FastAPI", "AWS", "Docker"]
["Kubernetes", "Terraform", "SageMaker"]
["BERT", "LSTM", "MLOps", "Kafka"]

$kubectl get nodes
STATUS: Ready UPTIME: 99.9%

$python deploy_model.py --env prod
▸ Building Docker image... done
▸ Pushing to ECR... done
▸ Deployment successful! 🚀

$_
01

About Me

I'm a Python Developer and AWS Cloud Engineer specializing in backend systems, cloud infrastructure, DevOps automation, and MLOps pipelines. With 3+ years of hands-on experience at Amazon, NextCS Optima, and Broadway Infotech, I've built production-grade systems using Python, FastAPI, Django, AWS services (Lambda, ECS, EKS, SageMaker), Docker, Kubernetes, and Terraform that handle terabytes of data daily.

My expertise spans the full cloud-native stack — from designing FastAPI microservices and orchestrating Kubernetes clusters to deploying BERT/LSTM models on SageMaker. I'm passionate about automation, observability, and building systems that are both scalable and cost-efficient.

Based in Noida, India · Open to remote and onsite opportunities globally.

Get in Touch View Resume ↗
45% Faster Model Deployment
35% Reduced Inference Time
20% Cloud Cost Savings
50% Faster Env Provisioning
02

Technical Skills

🐍
Backend
Python Development
PythonFastAPIREST APIs SQLAlchemyJWT/OAuthPyTest PostgreSQLSQL
☁️
Cloud
AWS Cloud
EC2S3Lambda API GatewayECS/EKSSageMaker GlueStep FunctionsBedrock CloudFormationIAM
⚙️
DevOps
CI/CD & Infrastructure
DockerKubernetesJenkins TerraformGitHub Actions Blue-Green DeployRolling Deploy CloudFormation
🤖
AI/ML
Machine Learning & MLOps
MLOpsBERTLSTM CNN/RNNNLPPyTorch MLflowModel Versioning Retraining Pipelines
📊
Observability
Monitoring & Alerting
PrometheusGrafana CloudWatchELK Stack
🔁
Data Engineering
Streaming & Pipelines
Apache KafkaApache Flink Apache SparkAWS Glue ETL PipelinesGreat Expectations
03

Work Experience

NOV 2025 – PRESENT
Software Developer
@ Broadway Infotech
  • Develop and maintain scalable, cloud-native backend systems using Python and AWS, improving system performance and reliability by +35%
  • Design and implement secure, high-performance RESTful APIs with FastAPI and SQLAlchemy, reducing latency by 20%
  • Architect fault-tolerant microservices on AWS Lambda, API Gateway, RDS, and EKS, achieving 99.9% uptime
  • Optimize database performance through advanced query optimization and schema tuning, enhancing response times by 30%
  • Automate CI/CD pipelines using Jenkins and Docker, increasing deployment speed by 40% and reducing release errors by 20%
  • Manage cloud infrastructure with Terraform, cutting environment setup time by 50%
  • Implement end-to-end observability using Prometheus, Grafana, and CloudWatch
MAY 2025 – OCT 2025
Software Engineer
@ NextCS Optima
  • Engineered scalable cloud-native backend and ML systems on AWS, ensuring 99.9% high availability
  • Developed and optimized backend APIs and microservices in Python and FastAPI, enhancing performance by 30%
  • Deployed serverless applications with AWS Lambda and containerized services on ECS; orchestrated Kubernetes workloads, boosting efficiency by 35%
  • Automated infrastructure provisioning with Terraform and Jenkins, reducing manual deployment time by 40%
  • Integrated ML models into backend services with FastAPI, Docker, AWS SageMaker and Lambda for scalable real-time inference
AUG 2021 – SEP 2023
AVOC Associate (ML IT-Data)
@ Amazon
  • Supported production ML systems by integrating Machine Learning, Data Engineering, and IT Operations, improving deployment efficiency by 20%
  • Deployed scalable ML models on AWS SageMaker and Lambda, reducing inference latency by 30%
  • Developed scalable ETL pipelines using Apache Spark, Kafka, and AWS Glue, processing over 3+ TB of data daily
  • Built real-time streaming pipelines with Kafka and Apache Flink, achieving latency under 200ms
  • Supported NLP applications (text classification, sentiment analysis), improving model accuracy by 18%
  • Enhanced model monitoring and automated retraining with Prometheus, MLflow, and Grafana, improving lifecycle management by 25%
04

Certifications

☁️
AWS Academy Cloud Foundations
Amazon Web Services
🤖
Generative AI Fundamentals
Databricks · Valid until April 2027
🐳
Docker Foundations Professional Certificate
Docker
🐍
Python Technology Association
Microsoft
05

Education

2023 – 2025 · M.Tech
Master of Technology — Computer Science
Galgotias University, Greater Noida
2016 – 2020 · B.Tech
Bachelor of Technology — Computer Science
KIIT University, Bhubaneswar
06

Research Publications

📄 CISES 2025 Conference (Offsite) Dec 2023 - Feb 2025

A Time-Series Forecasting Framework for Stock Market Prediction Using LSTM and Technical Indicators

  • 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%
LSTMTime-SeriesPyTorch TensorFlowTechnical IndicatorsMLOps AWS SageMakerDeep LearningFinancial ML
📄 CISES 2025 Conference (Offsite) Sep 2023 – Jun 2025

Hybrid Machine Learning Framework for Stock Market Forecasting: Integrating Technical Indicators

  • Designed a hybrid ensemble ML 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, improving model accuracy by +18%
  • Implemented real-time data ingestion pipeline using Apache Kafka and AWS Kinesis for live market data
  • Built scalable MLOps infrastructure with Docker and Kubernetes orchestration for model deployment
  • Created 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
    • XGBoostLightGBMRandom Forest Ensemble ModelsApache KafkaAWS Kinesis FastAPIEvidently AIOptuna
    07

    Get In Touch

    I'm actively looking for new opportunities in Python Backend Development, AWS Cloud Engineering, DevOps, and MLOps. Feel free to reach out — I respond quickly!

    pallavanand757@gmail.com +91-9431286412 linkedin.com/in/pallav-anand-8a32a625a github.com/pallavanand305 LeetCode Profile
    ✉ Send Message