Senior AI/ML Engineer

duction applications.

3. Model Tuning & Optimization :

- Fine-tune Small Language Models (SLMs) and LLMs for domain-specific tasks using techniques like LoRA, QLoRA, and PEFT to balance performance with computational efficiency.

- Optimize model inference latency and throughput for production environments (e.g., using ONNX, TensorRT).

4. MLOps & Deployment :

- Collaborate with DevOps to containerize models (Docker/Kubernetes) and deploy them via TorchServe, TensorFlow Serving, or Triton Inference Server.

- Implement experiment tracking and model registry workflows using MLflow or Weights & Biases (W&B).

5. Technical Leadership :

- Mentor junior developers and conduct code reviews.

- Translate complex business requirements into technical AI/ML specifications.