Description

Requirement/Must Have

8+ years of experience in data science or AI engineering.
2+ years of dedicated experience in Generative AI.
Deep expertise in PyTorch, TensorFlow, or JAX.
Hands-on experience with Hugging Face, LangChain, LlamaIndex, and vLLM.
Mastery of Python for performance tuning.
Deep understanding of regression, clustering, decision trees, and ensemble methods.
Strong grasp of Transformers, CNNs, RNNs, and reinforcement learning (RLHF).
Experience with Vector Databases such as ChromaDB, Pinecone, or Milvus.
Proficiency with SQL and NoSQL databases.
Experience with MLflow, Kubeflow, or Triton Inference Server.
Proficiency with AWS (SageMaker), Azure (Azure AI), or GCP (Vertex AI).
Experience with GPU acceleration and CUDA.

Responsibilities

Design and deploy Generative AI solutions using Large Language Models (LLMs) and diffusion models.
Implement optimization techniques including prompt engineering, fine-tuning, and Retrieval-Augmented Generation (RAG).
Develop predictive models by selecting, training, and tuning traditional machine learning and deep learning algorithms.
Build scalable pipelines for data preprocessing, feature engineering, and model training.
Optimize AI frameworks to improve inference speed, reduce latency, and lower compute costs.
Integrate AI models seamlessly into production software architectures and enterprise workflows.

Skills

Python.
Generative AI (GenAI).
Large Language Models (LLMs).
Prompt Engineering.
Fine-tuning LLMs.
Retrieval-Augmented Generation (RAG).
PyTorch.
TensorFlow.
JAX.
Hugging Face.
LangChain.
LlamaIndex.
vLLM.
Machine Learning.
Deep Learning.
Transformers.
CNNs.
RNNs.
RLHF.
Vector Databases.
MLOps.
CUDA.

Qualification And Education

Master s degree in Computer Science, Data Science, Mathematics, or a related quantitative field

Key Skills

Education

Any Graduate