Generative AI Architect

1 week ago


Delhi Gurugram NCR Noida, India 9yards It Technology Full time ₹ 15,00,000 - ₹ 30,00,000 per year

Key Responsibilities:

  • Define and own the architecture strategy for Generative AI and LLM-based solutions across multiple business domains.
  • Evaluate, select, and integrate AI frameworks, models, and platforms (e.g., OpenAI, Azure OpenAI, Vertex AI, Claude, Llama, etc.).
  • Architect and deploy end-to-end AI pipelines, including data ingestion, model training, fine-tuning, and inference.
  • Lead RAG (Retrieval-Augmented Generation), prompt engineering, and context management design for enterprise-grade solutions.
  • Collaborate with data engineering teams to design scalable data architectures that support AI workloads.
  • Drive POCs and pilot projects to assess model performance, latency, and cost efficiency.
  • Ensure compliance with AI governance, ethics, and data privacy standards.
  • Mentor and guide engineering teams in best practices for AI system design, MLOps, and deployment.
  • Stay abreast of emerging trends in Generative AI, deep learning, and NLP to continuously enhance solution design.

Required Skills & Qualifications:

  • Bachelors or Master's degree in Computer Science, Artificial Intelligence, Data Science, or related field.
  • 12–15 years of total experience in software development, architecture, or AI engineering — with at least 3–5 years in AI/ML solution design.
  • Proven expertise in Generative AI, NLP, and LLM-based architectures.
  • Hands-on experience with transformer models (e.g., GPT, Llama, Claude, Mistral, etc.) and frameworks such as LangChain, LlamaIndex, Hugging Face Transformers.
  • Strong proficiency in Python, with exposure to PyTorch, TensorFlow, or JAX.
  • Experience integrating AI systems with cloud platforms — AWS Sagemaker, Azure AI, or Google Vertex AI.
  • Familiarity with vector databases (Pinecone, FAISS, Chroma, Milvus) and embedding models.
  • Understanding of microservices architecture, APIs, and containerization (Docker/Kubernetes).
  • Excellent understanding of data engineering, model lifecycle management, and MLOps practices.
  • Strong leadership, communication, and stakeholder management skills

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