Description

Design and deploy generative AI solutions, building scalable pipelines and optimizing models for production environments.

This role is on-site.

Responsibilities

• Design and deploy Generative AI solutions using LLMs and diffusion models.
• Implement optimization techniques including prompt engineering, fine-tuning, and Retrieval-Augmented Generation (RAG).
• 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.

Required Skills

• 8+ years in data science or AI engineering, with 2+ years dedicated to Generative AI.
• 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: Deep understanding of regression, clustering, decision trees, ensemble methods, Transformers, CNNs, RNNs, and RLHF.
• Data Infrastructure: Experience with Vector Databases (ChromaDB, Pinecone, Milvus) and SQL/NoSQL.
• MLOps: 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).

Preferred Skills

• Master’s in Computer Science, Data Science, Mathematics, or a related quantitative field.

Key Skills

Education

Bachelor's or Master's degrees