Programming: Expert proficiency in Python and deep familiarity with asynchronous programming.
Deep Learning Frameworks: Extensive experience with PyTorch (preferred) or TensorFlow.
GenAI Stack: Hands-on experience with Hugging Face ecosystem (Transformers, Accelerate, PEFT, Diffusers) and LangChain or LlamaIndex.
Inference Optimization: Practical knowledge of quantization methods and high-throughput serving frameworks (e.g., vLLM).
Infrastructure: Proficiency with Docker, Kubernetes, and cloud platforms (AWS, GCP, or Azure) specifically for GPU-accelerated workloads.
Math & Theory: Strong foundation in linear algebra, probability, and the mathematical mechanics of Transformer architectures.

Generative AI Development:
Work with various model architectures (Transformer-based, Diffusion models, etc.) to solve complex business problems.
Evaluate model performance using specialized metrics (Perplexity, BLEU, ROUGE, or custom LLM-as-a-judge frameworks).
AI Inference Engineering:
Optimize model architectures for efficient deployment, focusing on reducing latency and memory footprint.
Implement inference optimization techniques including Quantization (GGUF, AWQ, GPTQ), Pruning, and Knowledge Distillation.
Utilize high-performance inference engines and frameworks such as TensorRT, vLLM, ONNX Runtime, or DeepSpeed-Inference.
Design scalable serving infrastructures using tools like NVIDIA Triton Inference Server, BentoML, or Ray Serve.
Optimize GPU utilization and memory management for large-scale model serving.
Data & Model Lifecycle:
Curate and preprocess large-scale datasets for pre-training, fine-tuning, and RLHF (Reinforcement Learning from Human Feedback).
Build automated pipelines for model evaluation and continuous monitoring of production models (detecting drift or hallucinations).
Collaborate with ML Engineers to integrate models into scalable microservices.

Role: IT & Information Security - Other

Senior Consultant - Data Scientist

Job Description & Responsibilities