The Software Design Engineer 2 (SDE-2) will contribute significantly to designing, developing, and optimizing scalable systems that support data science models and research workflows. The role requires converting research concepts into production-ready engineering solutions while following strong algorithmic principles, clean coding standards, and thorough documentation. The position involves close collaboration with data scientists, researchers, and cross-functional teams to build robust model pipelines and reusable components.

3. Principal Accountabilities

1. Algorithmic Understanding & Scalable Engineering Understand algorithms, data structures, and code complexity.Build scalable, optimized engineering solutions aligned with best practices. Quickly interpret new research papers, techniques, and model trends. Translate research ideas into functional engineering components.

2. Model & Component Development Develop POC components and reusable horizontal modules. Build and integrate model utilities, pipelines, and domain-specific solutions. Conduct logical validation and implement unit/integration test cases.

3. Code Quality, Documentation & Compliance Maintain detailed documentation of modules, parameters, and design decisions. Document research insights, experiment outcomes, and technical findings. Provide SonarQube and Checkmarks reports when required. Deliver clean, maintainable, and readable code.

4. Performance Optimization & Maintainability Ensure code meets performance and runtime expectations. Provide performance benchmarks and optimization insights Deliver well-structured, maintainable code and documentation for long-term usage.

4. Key Performance Indicators (KPIs)

1. Research Understanding & Innovation Speed and accuracy in understanding new research papers and technologies. Number of research concepts converted into functional prototypes. Contribution toward creating reusable and scalable components.

2. Component & Model Development Quality Timely delivery of POCs and engineering modules. High coverage of unit, integration, and functional tests. Fewer defects during testing and post-deployment. Successful POC outcomes in internal or customer evaluations.

3. Code Quality & Documentation Improved performance of models and pipelines (latency, throughput, efficiency). Reduced compute consumption (CPU, GPU, memory). Reliable and stable components in test/production environments. High code quality reflected in SonarQube / Checkmarx metrics. Clean, maintainable code with minimal review changes. Comprehensive documentation of modules, parameters, and research work. Optimization of algorithm runtime and complexity. Effective identification and resolution of bottlenecks.

5. Key Dimensions

Financial Accountability: Not applicable / As assignedDirect/Indirect Reports: None / As assignedGeographical Scope: India Gurugram

6. Knowledge, Skills & Experience

Educational QualificationsM.Tech / M.Sc

Relevant ExperienceExperience working with AI models Experience in Computer Vision, NLP, and Generative AI Knowledge of DBMS Hands-on experience with Python and AI libraries (PyTorch, Keras, etc.)

Skills RequiredPython (pandas, numpy, tensorflow, keras, pytorch) Machine Learning Computer Vision Natural Language Processing (NLP)DBMS Generative AI Strong problem-solving abilities

Key Skills: Artificial Intelligence, NLP, PyTorch, RDBMS

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Software Design Engineer / Data Scientist