Software Engineer/Research Engineer (AI Model Evaluation & Systems)

Intelliswift, an LTTS Company, is seeking a Software Engineer/Research Engineer to advance next-generation AI systems. The role focuses on designing benchmarks, test suites, automation frameworks, and evaluation pipelines, while collaborating with researchers and engineers to assess and improve AI model performance.

Responsibilities:

• Design, implement, and maintain evaluation frameworks for AI and machine learning systems
• Develop benchmarks, test suites, and validation workflows used to measure model performance
• Investigate model behavior, performance discrepancies, and system-level issues through data-driven experimentation
• Build scalable Python-based tooling and infrastructure to support AI research and evaluation
• Collaborate with researchers and cross-functional teams to improve model quality and performance
• Document findings, methodologies, and technical decisions to ensure reproducibility
• Contribute to deployment, monitoring, and operational excellence of AI systems
• Drive engineering best practices around testing, reliability, and code quality

Requirements:

• • Bachelor's degree in Computer Science, Engineering, Machine Learning, AI, or a related field
• • 1-3 years of experience in AI/ML engineering, research engineering, or software engineering
• • Strong programming skills in Python
• • Hands-on experience training and evaluating machine learning models
• • Experience with PyTorch and modern deep learning frameworks
• • Experience developing benchmarks, validation frameworks, or model evaluation workflows
• • Strong debugging, analytical, and problem-solving abilities
• • Demonstrated experience managing technical projects from implementation through validation and delivery
• • Familiarity with AI-assisted development tools such as Claude Code, Cursor, Codex, or GitHub Copilot
• • Experience with distributed training technologies such as DDP or FSDP
• • Knowledge of large language models (LLMs), generative AI, or AI agents
• • Experience with large-scale testing, developer tooling, or machine learning infrastructure
• • Exposure to cloud platforms such as AWS, Azure, or GCP
• • Experience with Docker, Kubernetes, CI/CD, or MLOps practices
• • Knowledge of Rust or TypeScript
• • Contributions to open-source projects, research publications, or personal technical projects