Lead AI Engineer

We are looking for a hands-on Lead AI Engineer to build and optimise Speech AI and Clinical AI solutions. The ideal candidate should have deep expertise in fine-tuning LLMs and ASR models, model evaluation, distributed training, and deploying production-grade AI systems.

Responsibilities:

• Fine-tune Speech AI (ASR) and LLM models for clinical applications.
• Build training datasets and evaluation pipelines.
• Optimise models for latency, scalability, and production deployment.
• Lead technical decisions, mentor engineers, and collaborate with Product, Clinical, and MLOps teams.
• Design robust AI systems using fine-tuning, RAG, and distributed training techniques.

Required Tech Stack:

• Programming: Python, SQL, Bash.
• ML Frameworks: PyTorch, Hugging Face (Transformers, PEFT, TRL, Accelerate).
• LLMs: Llama, Qwen, Mistral, Gemma.
• Fine-Tuning: LoRA, QLoRA, SFT, RLHF, DPO, ORPO, PPO.
• Speech AI: Whisper, wav2vec2 HuBERT, Conformer, NeMo, ESPnet, SpeechBrain.
• RAG and NLP: RAG, Vector Databases, Prompt Engineering, Clinical NLP.
• Distributed Training: DeepSpeed, PyTorch FSDP, Multi-GPU.
• Inference: ONNX, TensorRT, vLLM, Triton Inference Server.
• Cloud and MLOps: AWS/GCP/Azure, Docker, Kubernetes, MLflow/W& B, Git, CI/CD.