Generative AI Engineer

1 day ago

Bengaluru Gurugram, India Sourceright Technologies Full time
Job Description

- 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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