Software Design Engineer
Location
Department
Reporting To:
Job Purpose
The Software Design Engineer will play a key role in designing, developing, and optimizing scalable engineering systems that support data science models, research workflows, and AI-driven solutions. The role focuses on transforming research ideas into production-ready engineering components, ensuring strong algorithmic foundations, clean coding practices, performance optimization, and comprehensive documentation.
The engineer will work closely with data scientists, researchers, and cross-functional teams to build reliable, high-performance model pipelines, reusable components, and proof-of-concept (POC) solutions.
Key Responsibilities
1. Algorithmic Understanding & Scalable Engineering
- Apply strong knowledge of algorithms, data structures, and time/space complexity
- Design solutions that are scalable, optimized, and aligned with engineering best practices
- Quickly understand and implement new research papers, technologies, and model techniques
- Translate research concepts and experimental work into functional engineering solutions
2. Model & Component Development
- Develop proof-of-concept (POC) components and reusable horizontal modules
- Build and integrate model utilities, pipelines, and domain-specific solutions
- Support end-to-end development of AI/ML model pipelines
- Perform logical validation and implement unit and integration test cases
3. Code Quality, Documentation & Compliance
- Maintain detailed module-level documentation, including parameters and design rationale
- Document research findings, experiment summaries, and engineering decisions
- Ensure compliance with code quality and security standards
- Generate and maintain SonarQube and Checkmarx reports when required
- Deliver clean, readable, maintainable, and well-structured code
4. Performance Optimization & Maintainability
- Ensure solutions meet performance, scalability, and runtime requirements
- Provide performance benchmarks where applicable
- Optimize models and pipelines for latency, throughput, and resource utilization
- Identify and resolve performance bottlenecks in code and models
- Deliver stable, reliable components suitable for testing and production environments
Key Performance Indicators (KPIs)
- Speed and effectiveness in understanding and implementing research innovations
- Number of research ideas converted into working prototypes or solutions
- Quality and reusability of horizontal components
- Timely delivery of POCs, components, and engineering modules
- Test coverage across unit, integration, and functional tests
- Reduction in defects post-delivery
- Improvement in model and pipeline performance
- Code quality reflected through SonarQube and Checkmarx scores
- Adherence to optimal algorithmic performance standards
Education & Experience
- MTech / MSc (Computer Science, Data Science, AI, or related field preferred)
- Hands-on experience working with AI and machine learning models
- Experience in one or more of the following domains:
- Computer Vision
- Natural Language Processing (NLP)
- Generative AI (GenAI)
Technical Skills Required
- Python (NumPy, Pandas, TensorFlow, Keras, PyTorch)
- Machine Learning
- Computer Vision
- Natural Language Processing (NLP)
- Generative AI
- DBMS
- Strong problem-solving and analytical skills
Behavioural & Professional Skills
- Strong logical thinking and analytical capability
- Ability to collaborate with researchers and cross-functional engineering teams
- High ownership of deliverables and quality
- Ability to clearly document and explain technical decisions
- Proactive learner with interest in emerging AI and data science technologies
