GEN AI Architect

GEN AI Architect

Design and build advanced generative AI systems capable of learning, reasoning, and making autonomous decisions.

Responsibilities

• AI Systems Architecture: Design GenAI architecture and infrastructure, including data storage, processing, and retrieval; ensure scalability, flexibility, and efficiency.
• Algorithm Development: Develop and continuously improve ML/DL models (machine learning, deep learning, neural networks) to raise performance and accuracy.
• Data Integration: Identify and integrate data sources; design collection, cleansing, and transformation workflows with data scientists/engineers.
• Model Training & Evaluation: Train models using supervised, unsupervised, or reinforcement learning; implement evaluation methods and fine-tune based on feedback/insights.
• Neural Network Design: Build and optimize neural networks for NLP, computer vision, recommendations, and predictive analytics; improve accuracy and efficiency via architectures/techniques.
• System Integration: Collaborate with software engineers to integrate AI into existing platforms/apps; ensure compatibility and communication across components.
• Ethical & Responsible AI: Apply fairness, transparency, and accountability; mitigate bias and related risks.
• Research & Innovation: Stay current with AI advancements; run research/experimentation to enhance AI capabilities.
• Teamwork & Communication: Partner with cross-functional teams (data scientists, engineers, business stakeholders) and explain complex concepts to technical and non-technical audiences.

Mandatory Qualifications

• Bachelor’s or Master’s (or equivalent) in CS, Artificial Intelligence, or related field.
• Experience with large language models (LLMs) and prompt engineering.
• Experience designing/developing AI systems using ML/DL/neural networks.
• Strong programming skills in Python, R, or Java.
• Familiarity with AI frameworks such as TensorFlow, PyTorch, or Keras.
• Proven cloud experience (e.g., AWS, Azure, Google Cloud) and deploying AI models.
• Solid understanding of AI concepts, algorithms, and methodologies.
• Knowledge of large-scale AI solution design and data integration/cleansing/transformation.
• Strong analytical/problem-solving and collaborative communication skills.

Preferred Skills

• Knowledge of NVIDIA CUDA and cuDNN.