Description

Key Responsibilities

GenAI & Model Development

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.
Framework & Pipeline Engineering

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.
Technical Skills Required

AI/ML Frameworks & Libraries

Core Frameworks: Deep expertise in PyTorch, TensorFlow, or JAX.
GenAI Ecosystem: Hands-on experience with Hugging Face, LangChain, LlamaIndex, and vLLM.
Core Languages: Mastery of Python for performance tuning.
Algorithms & Math

Machine Learning: Deep understanding of regression, clustering, decision trees, and ensemble methods.
Deep Learning: Strong grasp of Transformers, CNNs, RNNs, and reinforcement learning (RLHF).
Data Infrastructure: Experience with Vector Databases (ChromaDB, Pinecone, Milvus) and SQL/NoSQL.
MLOps & Infrastructure

Deployment: Experience with MLflow, Kubeflow, or Triton Inference Server.
Cloud & Compute: Proficiency with AWS (SageMaker), Azure (Azure AI), or GCP (Vertex AI), alongside GPU acceleration (CUDA).
Experience & Qualifications

Experience: 8 years in data science or AI engineering, with 2 years dedicated to Generative AI.
Education: Master s in Computer Science, Data Science, Mathematics, or a related quantitative field

Key Skills

Education

Bachelor's or Master's degrees