Role Proficiency:
Independently develops error free code with high quality validation of applications guides other developers and assists Lead 1 – Software Engineering
Outcomes:
Understand and provide input to the application/feature/component designs; developing the same in accordance with user stories/requirements. Code debug test document and communicate product/component/features at development stages. Select appropriate technical options for development such as reusing improving or reconfiguration of existing components. Optimise efficiency cost and quality by identifying opportunities for automation/process improvements and agile delivery models Mentor Developer 1 – Software Engineering and Developer 2 – Software Engineering to effectively perform in their roles Identify the problem patterns and improve the technical design of the application/system Proactively identify issues/defects/flaws in module/requirement implementation Assists Lead 1 – Software Engineering on Technical design. Review activities and begin demonstrating Lead 1 capabilities in making technical decisionsMeasures of Outcomes:
Adherence to engineering process and standards (coding standards) Adherence to schedule / timelines Adhere to SLAs where applicable Number of defects post delivery Number of non-compliance issues Reduction of reoccurrence of known defects Quick turnaround of production bugs Meet the defined productivity standards for project Number of reusable components created Completion of applicable technical/domain certifications Completion of all mandatory training requirementsOutputs Expected:
Code:
Develop code independently for the above
Configure:
Test:
scenarios and execution
Domain relevance:
Manage Project:
Manage Defects:
Estimate:
effort
resource dependence for one's own work and others' work
including modules
Document:
Manage knowledge:
share point
libraries and client universities
Status Reporting:
Release:
Design:
Mentoring:
Skill Examples:
Explain and communicate the design / development to the customer Perform and evaluate test results against product specifications Develop user interfaces business software components and embedded software components 5 Manage and guarantee high levels of cohesion and quality6 Use data models Estimate effort and resources required for developing / debugging features / components Perform and evaluate test in the customer or target environment Team Player Good written and verbal communication abilities Proactively ask for help and offer helpKnowledge Examples:
Appropriate software programs / modules Technical designing Programming languages DBMS Operating Systems and software platforms Integrated development environment (IDE) Agile methods Knowledge of customer domain and sub domain where problem is solvedAdditional Comments:
Key Responsibilities: • Design, develop, and implement AI and machine learning models and algorithms tailored to solve specific business challenges. • Collaborate with software engineers, and other stakeholders to integrate AI solutions into existing applications and platforms. • Analyze large datasets to identify patterns and insights that can drive model development and optimization. • Optimize and fine-tune machine learning models for performance, scalability, and efficiency. • Stay current with the latest AI and machine learning research, tools, and techniques to ensure our solutions are cutting-edge. • Develop and maintain documentation for AI models, algorithms, and workflows. • Participate in code reviews, provide constructive feedback, and ensure best practices in AI and software development are followed. • Assist in troubleshooting and resolving issues related to AI models and systems in production environments. • Collaborate with product managers and stakeholders to understand business requirements and translate them into technical specifications for AI development. Qualifications: • Bachelor’s or Master’s degree in Computer Science, Data Science, Artificial Intelligence, or a related field. • 3-6 years of experience in AI development, machine learning, or a related field. • Strong programming skills in at least one of the following languages Python, R, Java, or C++, with the preferred language being Python • Proficiency with machine learning frameworks and libraries such as TensorFlow, PyTorch, scikit-learn, or Keras. • Experience with data preprocessing, feature engineering, and model evaluation techniques. • Familiarity with cloud platforms and services (particularly MS Azure) for deploying AI models. • Strong problem-solving skills and the ability to work independently and collaboratively in a team environment. • Excellent communication skills, with the ability to explain complex AI concepts to non-technical stakeholders. Preferred Qualifications: • Experience with natural language processing (NLP), or reinforcement learning. • Experience with version control systems (e.g., Git) and CI/CD pipelines. • Familiarity with DevOps practices and tools for AI model deployment and monitoring. • Bachelors or Masters degree in Computer Science Engineering or Equivalent