Generative AI Engineer (NV68FCI RM 3524)

Position: Generative AI Engineer (NV68FCI RM 3524)

Job Description:
Experience Requirements:

• 6+ years of overall data science or ML experience.
• 1.5+ years of hands-on experience with generative AI and large language models.
• Advanced degree in Computer Science, Machine Learning, or related field.
  · • Experience in taking ML/DL model to production.

Technical Expertise:

• Python, C++ or any other high-level language.
• PyTorch and TensorFlow.
• Experience with Hugging Face Transformers, LangChain, LlamaIndex, Pydantic AI
• Deep Learning fundamentals: neural networks, backpropagation, optimization
• Generative AI frameworks: OpenAI API, Anthropic Claude, Google Gemini, local LLM deployment
• Model architectures: Transformers, attention mechanisms, encoder-decoder models
• Fine-tuning techniques: LoRA, QLoRA, full parameter fine-tuning, RLHF
• Design and implement comprehensive evaluation pipelines
• A/B testing for AI systems and statistical significance testing
• Latency optimization techniques: model quantization, pruning, distillation
• Experience with MLOps tools: MLflow, Weights & Biases, DVC
  Cutting-Edge Technology:
• Access to premium GPU clusters A100s for training and experimentation
• Latest hardware and software tools to support your research and development
• Freedom to explore breakthrough AI technologies and methodologies
• Work on groundbreaking problems that haven’t been solved before
• Collaborate with world-class researchers and engineers from top universities and tech companies
• Contribute to open-source projects and publish your research

Core Responsibilities:

• Collaborate with cross-functional teams to translate complex AI concepts into practical business solutions.
• Design systems around open source, enterprise models like Llama, Gemini, GPT to solve innovative problems.
• Build robust evaluation pipelines to assess model performance, safety, and alignment with business objectives.
• Optimize model latency and performance for real-time applications and large-scale deployment
• Architect scalable ML infrastructure for training, inference, and model serving
• Fine-tune Large Language Models (LLMs) and other transformer-based architectures
• Ability to explain complex AI concepts to technical and non-technical stakeholders

Keywords
Python, Gen AI, LLM, Deep Learning, MLOps, LoRA, QLoRA, PyTorch

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