Highly skilled Senior Machine Learning Engineer with expertise in Deep Learning, Large Language Models (LLMs), and MLOps / LLMOps to design, optimize, and deploy cutting-edge AI solutions. The ideal candidate will have hands-on experience in developing and scaling deep learning models, fine-tuning LLMs / (e.g., GPT, Llama), and implementing robust deployment pipelines for production environments.
Responsibilities
Model Development & Fine-Tuning :
- Design, train, fine-tune and optimize deep learning models (CNNs, RNNs, Transformers) for NLP, computer vision, or multimodal applications.
- Fine-tune and adapt Large Language Models (LLMs) for domain-specific tasks (e.g., text generation, summarization, semantic similarity).
- Experiment with RLHF (Reinforcement Learning from Human Feedback) and other alignment techniques.
Deployment & Scalability (MLOps / LLMOps) :
Build and maintain end-to-end ML pipelines for training, evaluation, and deployment.Deploy LLMs and deep learning models in production environments using frameworks like FastAPI, vLLM, or TensorRT.Optimize models for low-latency, high-throughput inference (eg., quantization, distillation, etc.).Implement CI / CD workflows for ML systems using tools like MLflow, Kubeflow.Monitoring & Optimization :
Set up logging, monitoring, and alerting for model performance (drift, latency, accuracy).Work with DevOps teams to ensure scalability, security, and cost-efficiency of deployed models.Required Skills & Qualifications :
5-7 years of hands-on experience in Deep Learning, NLP, and LLMs.Strong proficiency in Python, PyTorch, TensorFlow, Hugging Face Transformers, and LLM frameworks.Experience with model deployment tools (Docker, Kubernetes, FastAPI).Knowledge of MLOps / LLMOps best practices (model versioning, A / B testing, canary deployments).Familiarity with cloud platforms (AWS, GCP, Azure).Preferred Qualifications :
Contributions to open-source LLM projects.