AI Innovator

1 day ago


Bhavnagar, Gujarat, India beBeeArtificialIntelligence Full time ₹ 1,80,00,000 - ₹ 2,50,00,000

We are seeking an experienced AI architect to lead the design, development, and deployment of large-scale artificial intelligence solutions.

Key Responsibilities:
  • Design end-to-end AI systems leveraging large language models and generative AI technologies.
  • Develop scalable, production-ready AI applications that meet business objectives and performance requirements.
  • Evaluate and integrate LLM APIs from leading providers (OpenAI, Anthropic Claude, Google Gemini, etc.).
  • Establish best practices for prompt engineering, model selection, and AI system optimization.
  • Fine-tune open-source models (Llama, Mistral, etc.) for specific business use cases.
  • Implement custom training pipelines and evaluation frameworks.
  • Optimize model performance, latency, and cost for production environments.
  • Stay current with the latest model architectures and fine-tuning techniques.

Technical Skills:

  • Expert-level proficiency with LLM APIs (OpenAI, Claude, Gemini, etc.).
  • Hands-on experience fine-tuning transformer models (Llama, Mistral, etc.).
  • Strong proficiency in FastAPI, Docker, and Kubernetes.
  • Experience with ML frameworks (PyTorch, TensorFlow, Hugging Face Transformers).
  • Proficiency in Python and modern software development practices.
  • Experience with cloud platforms (AWS, GCP, or Azure) and their AI/ML services.

Required Qualifications:

  • 7+ years of experience in machine learning engineering or data science.
  • Proven track record of delivering large-scale ML solutions.

Benefits:

  • Collaborate with stakeholders to understand business problems and translate them into technical requirements.
  • Provide technical guidance and mentorship to development teams.
  • Conduct feasibility assessments and technical due diligence for AI initiatives.
  • Create technical documentation, architectural diagrams, and implementation roadmaps.

Preferred Qualifications:

  • Experience with vector databases and retrieval-augmented generation (RAG) systems.
  • Knowledge of distributed training and model parallelization techniques.
  • Experience with model quantization and optimization for edge deployment.
  • Familiarity with AI safety, alignment, and responsible AI practices.

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