AI Solution Architect

1 day ago

Ahmedabad, Gujarat, India beBeeResponsibility Full time ₹ 1,50,00,000 - ₹ 2,50,00,000

Transform technology by crafting innovative AI solutions.

Key Responsibilities

  • Design and Deploy AI Solutions: Utilize expertise in Generative AI and Large Language Models to develop cutting-edge applications that drive business growth and efficiency.
  • Fine-tune and Optimize LLMs: Apply knowledge of prompt engineering to fine-tune and optimize models for domain-specific tasks, resulting in improved performance and accuracy.
  • Integrate LLMs with APIs and Applications: Seamlessly integrate models with various APIs, databases, vector stores, and enterprise applications to create robust and scalable AI solutions.
  • Evaluate and Implement Model Deployment Strategies: Collaborate with cross-functional teams to evaluate and implement strategies across on-prem, cloud, serverless, or containerized environments.
  • Cross-Functional Collaboration: Work closely with data engineers, product managers, and software engineers to deliver end-to-end solutions that meet business objectives.
  • Staying Updated with AI Advancements: Stay current with the latest research and advancements in AI, proposing adoption of new technologies to enhance competitiveness.

Required Qualifications

  • Software Development Experience: Proven track record of delivering high-quality software solutions, with a preference for Python experience.
  • Generative AI and LLM-based Development: At least 2 years of experience developing applications, with a proven track record of delivering production-grade projects.
  • AI Fundamentals: Deep understanding of NLP, deep learning, embeddings, and transformers, with experience working with popular frameworks like LangChain or Haystack.
  • Cloud Platforms and AI Services: Hands-on experience with cloud platforms (AWS, Azure, GCP) and AI services (Bedrock, VertexAI), with proficiency in containerization and deployment tools (Docker, Kubernetes).
  • MLOps Tools and Responsible AI Practices: Experience with MLOps tools (MLflow, Kubeflow) for model lifecycle management, with a strong focus on responsible AI practices and compliance.

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