Senior Software Engineer-2

Build and own Java services/APIs that deliver AI-powered features, ensuring functional correctness, performance, and maintainability in a multi-tier, distributed environment.
Develop hands-on AI components in Python and productionise them, contributing to model workflows that support training/tuning and/or inference use cases.
Implement and operate AI systems in production, including deployment frameworks, automation for training/testing/deploy/update, and model versioning/monitoring to sustain high-quality outputs over time.
Own delivery across the lifecycle: estimate and deliver design/dev/test/deploy/config/documentation work; automate build and run aspects of software.
Drive engineering excellence through code/test/automation reviews, adoption of standards and best practices, and pragmatic design trade-offs within the team.
Troubleshoot complex issues spanning services and AI components, applying strong problem-solving, triage, and root-cause discipline.
Mentor engineers via technical guidance and hands-on reviews (without people-management responsibilities), raising the bar on quality, operability, and AI readiness.


All About You (Must-Have Skills)
The ideal candidate demonstrates advanced, hands-on expertise across the areas below. (Per template guidance: do not include years-of-experience; focus on proficiency.)
Strict Must-Haves (Non-negotiable)

Python – MUST: proven hands-on Python engineering for AI workloads (writing production code, tests, packaging, and operationalising components).
AI Frameworks – MUST: hands-on experience with one or more modern AI frameworks (e.g., PyTorch / TensorFlow / Hugging Face) used for building, tuning, or serving models.

Core Engineering Must-Haves

Strong Java engineering (designing, coding, testing, maintaining software; building scalable and efficient solutions; debugging complex issues).
Proven ability to deliver scalable, reliable software with strong testing discipline (unit/integration/other testing mechanisms) and operational mindset.
Hands-on experience with production AI/ML lifecycle elements such as model deployment, automation of workflows, and monitoring performance over time.
Strong communication and precision in technical discussions; ability to collaborate effectively across roles and geographies.