DevOps Engineer – AWS (2 Years Experience)

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Job Description

Superport-IT is seeking a DevOps Engineer with 2+ years of hands-on experience in AWS to join our growing Cloud Engineering team. The ideal candidate is passionate about automation, scalable infrastructure, and continuous delivery. You’ll work with a skilled team to design and implement DevOps pipelines, support cloud infrastructure, and contribute to modernizing our deployment workflows.

This role is perfect for someone who has foundational AWS DevOps experience and is looking to take the next step in their cloud career with real-world projects and mentorship from senior engineers.

Key Roles & Responsibilities

  • Build and manage CI/CD pipelines using tools like Jenkins, GitHub Actions, or AWS CodePipeline
  • Automate infrastructure provisioning using Infrastructure as Code (IaC) tools like Terraform or AWS CloudFormation
  • Monitor and maintain cloud infrastructure using Amazon CloudWatch, SNS, and other AWS native tools
  • Work with development teams to streamline code deployments, testing, and releases
  • Administer and troubleshoot AWS services such as EC2, S3, RDS, IAM, Lambda, and VPC
  • Implement and maintain Docker containers and basic orchestration
  • Follow security best practices across cloud infrastructure
  • Participate in regular backups, disaster recovery planning, and system documentation
  • Collaborate in agile teams using version control (Git) and ticketing systems (JIRA, Trello)

Required Skills & Qualifications

  • 2+ years of experience in DevOps or Cloud Infrastructure, specifically in AWS
  • Experience with AWS core services like EC2, S3, IAM, RDS, CloudWatch, Lambda
  • Hands-on experience with CI/CD tools such as GitHub Actions, Jenkins, AWS CodeBuild/CodeDeploy
  • Proficient in at least one scripting language: Bash, Python, or PowerShell
  • Knowledge of Docker and container lifecycle
  • Basic understanding of networking, security groups, and VPC in AWS
  • Familiarity with source control systems like Git
  • Excellent communication and documentation skills
  • AWS certification (e.g., AWS Certified DevOps Engineer Associate or Cloud Practitioner) is a plus
Job Category: DevOps & Cloud Engineering
Job Type: Full Time
Job Location: Hybrid Remote
Positions Opened: 1

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Data Science Foundation Track

Course Curriculum: Your Journey from Novice to Data Scientist

Our curriculum is meticulously designed to take you on a step-by-step journey through the world of data science. Each module builds upon the last, blending core theory with practical, hands-on projects to ensure you don’t just learn—you learn by doing.

Module 1: Kickstart & Python Fundamentals (Week 1-2)

Objective: Build the foundational launchpad for your data science journey.

1.1 Introduction to Data Science:
  • What is Data Science, AI, and Machine Learning?
  • Roles & Responsibilities: Data Analyst vs. Data Scientist vs. ML Engineer.
  • The Data Science Lifecycle: From Business Problem to Deployed Solution.
1.2 Python Programming Essentials:
  • Setting up Your Environment: Anaconda, Jupyter Notebooks & VS Code.
  • Python Basics: Variables, Data Types, Operators, and Control Flow (Loops & Conditionals).
  • Core Data Structures: Lists, Tuples, Dictionaries, and Sets.
  • Functions and Object-Oriented Programming (OOP) Concepts.
  • Project 1: Create a simple command-line application (e.g., a calculator or a text-based game) to solidify Python programming logic.

Module 2: Data Analysis with NumPy & Pandas (Week 3-4)

Objective: Learn to manipulate, clean, and analyze complex datasets with industry-standard libraries.

2.1 Numerical Computing with NumPy:
  • Introduction to NumPy Arrays and their advantages over Python Lists.
  • Array Indexing, Slicing, and Mathematical Operations.
  • Statistical functions and Linear Algebra basics.
2.2 Data Manipulation with Pandas:
  • Introduction to Pandas Series and DataFrames.
  • Importing Data: Reading from CSV, Excel, and other file formats.
  • Data Cleaning: Handling Missing Values, Duplicates, and Inconsistent Data.
  • Indexing, Filtering, and Sorting DataFrames (iloc, loc).
  • Grouping and Aggregation with groupby().
  • Project 2: Take a messy, real-world dataset (e.g., sales data) and perform a full data cleaning and initial analysis to uncover key insights.
Module 3: Database Management with SQL (Week 5)

Objective: Master the art of querying databases to extract exactly the data you need.

3.1 Relational Database Fundamentals:
  • Understanding Tables, Primary Keys, and Foreign Keys.
3.2 Essential SQL Queries:
  • SELECT, FROM, WHERE for data retrieval.
  • GROUP BY, HAVING, ORDER BY for data aggregation and sorting.
3.3 Advanced SQL:
  • Joining multiple tables (INNER, LEFT, OUTER JOINs).
  • Subqueries and Common Table Expressions (CTEs).
  • Project 3: Answer complex business questions by writing SQL queries against a sample relational database (e.g., an e-commerce store database).
Module 4: Data Visualization & Storytelling (Week 6)

Objective: Transform raw data into compelling visual stories that drive business decisions.

4.1 Principles of Effective Visualization:
  • Choosing the right chart for your data.
  • The art of storytelling with data.
4.2 Visualization with Matplotlib & Seaborn:
  • Creating basic plots with Matplotlib (Line, Bar, Scatter).
  • Building advanced statistical plots with Seaborn (Heatmaps, Box Plots, Violin Plots).
  • Customizing plots for professional reports.
4.3 Interactive Dashboards:
  • Introduction to interactive plotting with libraries like Plotly.
  • Project 4: Create a multi-chart dashboard to present the findings from your Project 2 dataset, telling a clear story about the insights you discovered.
Module 5: Essential Statistics & Probability (Week 7)

Objective: Understand the statistical concepts that form the backbone of all data science models.

5.1 Descriptive Statistics:
  • Measures of Central Tendency (Mean, Median, Mode).
  • Measures of Dispersion (Variance, Standard Deviation).
5.2 Probability Distributions:
  • Understanding Normal, Binomial, and Poisson distributions.
5.3 Inferential Statistics:
  • Hypothesis Testing, P-values, and Confidence Intervals.
  • Introduction to A/B Testing for business decision-making.
  • Project 5: Analyze the results of a sample A/B test to determine if a change to a website resulted in a statistically significant improvement.
Module 6: Machine Learning Fundamentals (Week 8-9)

Objective: Build and evaluate your first predictive models.

6.1 Introduction to Machine Learning:
  • Supervised vs. Unsupervised vs. Reinforcement Learning.
  • The Train-Test Split and Model Evaluation Metrics.
6.2 Regression Models:
  • Linear Regression: Predicting continuous values (e.g., house prices).
6.3 Classification Models:
  • Logistic Regression: Predicting binary outcomes (e.g., customer churn).
  • K-Nearest Neighbors (KNN) and Decision Trees.
  • Project 6: Build two models: one to predict house prices from a real estate dataset and another to predict customer churn from a telecom dataset.
  • Module 7: Advanced Machine Learning & Capstone Project (Week 10-12)

Objective: Tackle complex problems with advanced algorithms and complete a portfolio-worthy project.

7.1 Advanced Techniques:
  • Ensemble Methods: Random Forests and Gradient Boosting (XGBoost).
  • Unsupervised Learning: K-Means Clustering for customer segmentation.
  • Introduction to Feature Engineering.
7.2 Capstone Project:
  • Choose from a selection of real-world business problems.
  • Apply the entire data science lifecycle: Data Acquisition, Cleaning, EDA, Modeling, and Interpretation.
  • Present your findings and methodology in a final report.
7.3 Introduction to Deployment:
  • Learn how to build a simple, interactive web app for your model using Streamlit.
Module 8: Career Accelerator Workshop (Exclusive to the ₹58,000 Program)

Objective: Polish your professional profile and prepare for the job market.

8.1 Building Your Professional Brand:
  • Crafting a high-impact, keyword-optimized CV/Resume.
  • Optimizing your LinkedIn profile to attract recruiters.
8.2 Acing the Interview:
  • Technical Interview Preparation (SQL, Python, ML Theory).
  • Live Mock Interviews with personalized feedback.
8.3 Internship & Placement:
  • Onboarding for your guaranteed internship.
  • Workshops on abroad assistance (SOP/LOR writing) and job application strategies.