Lead AI Engineer

Technical

• 3-6 years of hands-on experience in ML/DL, with real production deployments (not just Kaggle medals).
• Strong system design skills, you can architect complex asynchronous systems and reason about failure modes, throughput, and latency.
• Deep familiarity with transformer-based architectures: LLMs, VLMs, SAM, and the foundational building blocks (CNNs, RNNs, attention mechanisms).
• Basic understanding of TTS and STT pipelines and current state-of-the-art models in the speech domain.
• Solid experience with PyTorch. You write clean, maintainable model code.
• You write production-grade, scalable code, not notebook prototypes. Proper abstractions, error handling, testing, and documentation are non-negotiable.
• Proficiency in Python. C++ is a strong plus.
• Hands-on experience deploying and optimizing models on AWS or GCP for real-time inference.
• Familiarity with inference engines like vLLM.
• Comfortable using AI-assisted development tools (Claude Code, Cursor, etc.) to accelerate development velocity.
• Actively keeps up with the fast-moving AI landscape, new papers, models, frameworks, and tooling. You know what shipped last month, not just last year.

Nice to Have

• Experience with training and fine-tuning LLMs and VLMs.
• Deeper experience in speech/audio processing beyond basic TTS/STT pipelines.
• Experience with edge deployment and model optimization for on-device inference.

Non-Technical

• Experience leading small engineering teams. You don't need a management title, you need to have actually done it.
• Startup experience. You know what it means to operate with limited resources and high ambiguity.
• Strong ownership mindset : you see problems and fix them without being asked.
• Clear, direct communication. You can hold your own in conversations with product, design, and leadership.
• Goal-oriented. You care about outcomes, not activity