Different Roles in AIML Domain

Different Roles in Machine Learning Pipeline

1. Business Analyst (BA)

A Business Analyst connects the business team and the technical team.

They understand business problems and convert them into technical requirements.

They focus more on:

• Business goals
• Requirement gathering
• Communication
• Documentation
• Process analysis

They usually do less coding.

What They Understand

• What problem the company is facing
• What solution is required
• What the business expects
• What success looks like
• What data is available

Example

Company Problem

Customers are leaving the platform.

Business Analyst Asks

• Why are customers leaving?
• What data do we have?
• Can AI predict customer churn?
• What should be the business goal?
• How will success be measured?

Expertise

• Business understanding
• Requirement analysis
• Process mapping
• Communication
• Documentation
• Stakeholder management

Skills

Technical Skills

• Excel
• SQL basics
• Power BI
• Tableau
• Documentation tools

Non-Technical Skills

• Communication
• Presentation
• Critical thinking
• Problem solving
• Team coordination

2. Data Analyst

A Data Analyst studies data and finds useful insights.

They help companies understand what is happening in the business.

They mainly work on:

• Reports
• Dashboards
• Trends
• Visualizations
• Business insights

They answer:

“What is happening in the business?”

What They Analyze

• Sales performance
• Customer behavior
• Profit trends
• Business growth
• Product performance

Example

Business Questions

• Which product sells the most?
• Which city gives maximum profit?
• Why did sales decrease?
• Which customers buy frequently?

Data Analyst Creates

• Charts
• Dashboards
• Reports
• KPI (Key Performance Indicator) summaries

Expertise

• Data visualization
• Reporting
• Statistical analysis
• Trend analysis
• KPI analysis

Skills

Technical Skills

• SQL
• Excel
• Power BI
• Tableau
• Python basics
• R basics
• Statistics

Non-Technical Skills

• Analytical thinking
• Communication
• Reporting
• Attention to detail

3. Data Engineer

A Data Engineer builds systems for collecting, storing, and processing data.

They create the data infrastructure used by analysts and scientists.

They mainly work on:

• Data pipelines
• Data storage
• Big data systems
• Data processing
• Cloud platforms

They answer:

“How do we handle huge amounts of data efficiently?”

What They Build

• ETL pipelines
• Data warehouses
• Data lakes
• Streaming systems
• Cloud data systems

They help to maintain the Data Flow through

Website → API → Kafka → Spark → Data Warehouse

Data Engineer Tasks

• Collect data from APIs
• Move data automatically
• Store large datasets
• Process real-time data

Expertise

• Databases
• Distributed systems
• Big data technologies
• Data warehousing
• Cloud computing

Skills

Technical Skills

• SQL
• Python
• Spark
• Hadoop
• Kafka
• Airflow
• AWS
• GCP
• Azure

Non-Technical Skills

• Problem solving
• System thinking
• Optimization
• Team collaboration

Important Concepts

• ETL → Extract, Transform, Load
• ELT → Extract, Load, Transform
• AWS → Amazon Web Services
• GCP → Google Cloud Platform

4. Data Scientist

A Data Scientist builds intelligent systems using Machine Learning and statistics.

They create models that can predict, classify, recommend, and analyze data.

They answer:

“What will happen in the future?”

What They Build

• Prediction models
• Recommendation systems
• Fraud detection systems
• Forecasting systems
• AI applications

Example Applications

• Fraud detection
• Medical diagnosis
• Stock prediction
• Customer recommendation

Data Scientist Tasks

• Analyze data
• Train ML models
• Evaluate performance
• Improve accuracy

Expertise

• Machine Learning
• Statistics
• Mathematics
• AI algorithms
• Data analysis

Skills

Technical Skills

• Python
• R
• SQL
• Scikit-learn
• TensorFlow
• PyTorch

Non-Technical Skills

• Critical thinking
• Research mindset
• Problem solving
• Communication

Important Concepts

• Regression
• Classification
• Clustering
• Deep Learning
• Feature Engineering

5. ML Engineer (Machine Learning Engineer)

An ML Engineer converts ML models into real applications.

They make models scalable, fast, and usable in production.

They mainly focus on:

• Deployment
• APIs
• Optimization
• Scalability
• Reliability

They answer:

“How do we use ML models in real systems?”

What They Build

• Prediction APIs
• ML applications
• Model serving systems
• Automated pipelines

Example Application Flow

Mobile App → API → ML Model → Prediction

ML Engineer Tasks

• Deploy trained models
• Create APIs
• Optimize speed
• Improve reliability

Expertise

• Software engineering
• ML deployment
• API development
• Cloud deployment
• Optimization

Skills

Technical Skills

• Python
• Flask
• FastAPI
• Docker
• Kubernetes
• CI/CD
• Cloud deployment

Important Concepts

• Model serving
• Containerization
• API integration
• Deployment pipelines

Non-Technical Skills

• Problem solving
• System design
• Collaboration
• Debugging

6. DevOps Engineer

A DevOps Engineer manages infrastructure, deployment, and automation.

They ensure software systems run smoothly and reliably.

They mainly focus on:

• Automation
• Deployment
• Servers
• Infrastructure
• Monitoring

They answer:

“How do we deploy and maintain systems efficiently?”

What They Manage

• Servers
• Deployment pipelines
• Infrastructure
• Cloud platforms
• Automation systems

Example Automated Flow

Code Push → Testing → Deployment

DevOps Tasks

• Automate deployments
• Manage servers
• Monitor applications
• Maintain cloud systems

Expertise

• Cloud infrastructure
• Automation
• Linux systems
• Deployment pipelines

Skills

Technical Skills

• Linux
• Docker
• Kubernetes
• Jenkins
• GitHub Actions
• AWS
• Azure
• GCP

Important Concepts

• Infrastructure automation
• Containerization
• Continuous deployment

Non-Technical Skills

• Troubleshooting
• Team collaboration
• Monitoring
• System management

7. MLOps Engineer

An MLOps (Machine Learning Operations) Engineer manages Machine Learning systems after deployment.

They automate ML workflows and monitor model performance.

They combine:

• Machine Learning
• DevOps
• Automation

They answer:

“Is the ML model still performing correctly?”

What They Monitor

• Model accuracy
• Data drift
• Pipeline failures
• Retraining processes
• Experiment tracking

Example MLOps Tasks

• Detect accuracy drop
• Retrain models automatically
• Monitor prediction quality
• Track ML experiments

Expertise

• ML lifecycle automation
• Monitoring systems
• Production ML systems
• Pipeline management

Skills

Technical Skills

• MLflow
• Kubeflow
• Docker
• Kubernetes
• CI/CD
• Cloud ML tools

Non-Technical Skills

• Automation thinking
• Monitoring
• Debugging
• Collaboration

Important Concepts

• Model monitoring
• Retraining pipelines
• Experiment tracking

8. Database Administrator (DBA)

A DBA manages and maintains databases.

They ensure databases are secure, fast, and reliable.

They mainly focus on:

• Database performance
• Security
• Backup
• Recovery
• Access control

They answer:

“How do we keep databases secure and efficient?”

What They Manage

• Database servers
• User permissions
• Backups
• Replication
• Database security

Example DBA Tasks

• Optimize database queries
• Create backups
• Recover lost data
• Manage user access

Expertise

• Database optimization
• Query tuning
• Security management
• Backup systems

Skills

Technical Skills

• MySQL
• PostgreSQL
• Oracle
• MongoDB

Non-Technical Skills

• Attention to detail
• Monitoring
• Problem solving
• Reliability management

Important Concepts

• Indexing
• Replication
• Backup
• Recovery
• Security