Understanding AI Concepts: Develop a solid understanding of fundamental AI concepts, such as machine learning and basic algorithms.
Data Preprocessing: Clean and preprocess data for machine learning models. This may include handling missing values, scaling features, and transforming data into a suitable format.
Implementing Machine Learning Models: Build and implement basic machine learning models using popular Python libraries such as scikit-learn or TensorFlow.
Model Evaluation: Evaluate the performance of machine learning models using appropriate metrics. This involves assessing accuracy, precision, recall, F1 score, etc.
Feature Engineering: Engage in feature engineering to enhance the effectiveness of machine learning models by selecting relevant features or creating new ones.
Basic Natural Language Processing (NLP): Able to work on basic NLP tasks, such as text classification or sentiment analysis using libraries like NLTK or spaCy.
Debugging and Troubleshooting: Identify and resolve issues related to model performance, data quality, or code implementation.
Version Control: Use version control systems like Git to manage and track changes in code.
Documentation: Document code, processes, and methodologies to ensure clear communication within the team and for future reference.
Collaborate with Cross-Functional Teams: Work with other teams, such as product management, UX/UI design, and software engineering, to integrate AI solutions into larger software systems.
Stay Informed: Keep up to date with the latest developments in AI and machine learning by reading research papers, attending conferences, and following relevant blogs and forums.
Job Responsibility:
Key Performance Areas (KPAs):
Code Quality and Efficiency: Write clean, efficient, and maintainable code in Python. Adhere to coding standards and best practices. Optimize code for performance and resource utilization.
Problem Solving: Demonstrate strong problem-solving skills in developing software solutions. Debug and troubleshoot issues effectively.
Software Development Life Cycle (SDLC) Adherence: Follow the complete software development life cycle, including requirements analysis, design, coding, testing, and deployment.
Collaboration and Communication: Effectively communicate and collaborate with team members. Participate in code reviews and provide constructive feedback.
Project Delivery: Meet project deadlines and deliver high-quality solutions. Manage tasks and priorities effectively.
Technical Skills: Demonstrate proficiency in Python programming. Stay updated on relevant frameworks, libraries, and tools.
Version Control: Use version control systems (e.g., Git) effectively to manage codebase changes.
Testing and Quality Assurance: Write and execute unit tests to ensure code reliability. Participate in the development and execution of quality assurance processes.
Continuous Learning: Stay informed about new technologies and industry trends. Continuously improve skills and learn new Python-related technologies.
Documentation: Maintain thorough documentation for code, processes, and systems. Ensure documentation is clear, concise, and accessible to team members.
Adaptability: Adapt to changes in project requirements, technologies, and team dynamics.
Key Performance Indicators (KPIs):
Code Quality and Efficiency: Number of code defects identified and resolved. Code performance metrics (e.g., response time, resource utilization).
Problem Solving: Average time taken to resolve reported issues. A number of successfully resolved bugs or issues.
Collaboration and Communication: Participation in team meetings and discussions. Frequency and quality of communication with team members.
Project Delivery: On-time delivery of project milestones. Client or end-user satisfaction with project deliverables.
Version Control: Number of successful code merges and conflict resolutions.
Testing and Quality Assurance: Number of critical bugs identified during testing.
Continuous Learning: Number of new technologies or tools learned.
Documentation: Completeness and accuracy of documentation. Accessibility and usefulness of documentation to the team.
Adaptability: Successful adaptation to changes in project requirements.
Customer Focus: Number of user-reported issues resolved. Client or end-user feedback and satisfaction.
Code Reviews: Percentage of code reviewed within specified time frames.
Required Skills & Qualification:
Minimum bachelor’s degree in computer science.
At least 6 months of experience as a Python developer.
Knowledge of OOPs in Javascript, C & C++
Sound knowledge in HTML, CSS, JavaScript & JQuery.
Knowledge of the Django framework
Understanding of NoSQL databases like MongoDB.
Creating database schemas that represent and support application processes.
Ability to write maintainable, pluggable, modular, clean code with a detailed understanding of business logic.
Strong analytical skills and ability to “think outside of the box”
Excellent planning, organization, and time management skills
Superb interpersonal, communication, and collaboration skills.
