Advanced AI Architect

2 days ago


Amrāvati, Maharashtra, India beBeeArtificial Full time ₹ 20,00,000 - ₹ 30,00,000
About the Position

We are seeking an accomplished AI Architect with a proven track record in enterprise-scale AI solutions. The ideal candidate will have extensive experience in fine-tuning foundational models, and a strong understanding of cloud-native technologies.

Key Responsibilities
  • Solution Architecture: Develop and implement GenAI solutions using LLMs and advanced conversational AI frameworks.
  • Model Development: Fine-tune LLMs and SLMs using techniques like PEFT, LoRA, and QLoRA for domain-specific use cases.
  • Open-Source Frameworks: Utilize tools like Hugging Face, LangChain, LangGraph, and AutoGen to build scalable GenAI applications.
  • GCP Expertise:
  • Design and deploy AI solutions using GCP services such as Vertex AI, BigQuery, AI Platform, and Cloud Functions.
  • Optimize model performance, cost-efficiency, and reliability within GCP environments.
  • Integration and Deployment:
  • Integrate generative AI models into enterprise systems and applications.
  • Implement robust MLOps practices, including CI/CD pipelines (e.g., GitHub), and containerization using Docker and Kubernetes.
  • Coding and Development: Write clean, maintainable Python code for GenAI applications
  • Data Preprocessing: Build and maintain data pipelines for training and fine-tuning models.
  • Model Evaluation: Evaluate model performance using relevant metrics and benchmarks; iterate to improve outcomes.
Requirements
  • Bachelor's, Master's, or PhD in Computer Science, AI, Electrical Engineering, or Applied Mathematics.
  • 15–18 years of experience in software architecture and AI/ML systems.
  • Expert-level experience with Google Cloud Platform (GCP) and strong expertise in Microsoft Azure and Amazon Web Services (AWS).
  • Proficiency in Python, with deep knowledge of ML/DL frameworks like PyTorch and TensorFlow.
  • Experience in fine-tuning and deploying LLMs using modern techniques and tools.
  • Familiarity with MLOps platforms and workflows (MLFlow, Kubeflow, Airflow).
  • Excellent communication and leadership skills, with the ability to influence cross-functional teams and drive innovation.


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