Gen AI/ML Developer - Python, LLM, MLOps, Cloud

We are seeking a talented GenAI ML Engineer to develop and deploy cutting-edge generative AI solutions. This role focuses on building innovative applications, fine-tuning LLMs, and implementing robust machine learning pipelines to solve complex business problems using state-of-the-art advances in artificial intelligence.

• Generative AI & LLM Engineering: Design, fine-tune, and optimize large language models (GPT, Claude, Llama) for text, code, and multimodal applications.
  

• Model Training & Evaluation: Develop custom training pipelines for foundation model adaptation and implement frameworks to ensure model performance and safety.
  

• Application Development: Build robust ML applications, APIs, and services using Python and frameworks like LangChain or LlamaIndex.
  

• Prompt Engineering: Create and optimize sophisticated prompts to drive various LLM use cases.
  

• Production Deployment (MLOps): Deploy and monitor models in production environments, ensuring proper scaling and GPU utilization.
  

• Innovation: Stay current with GenAI research to implement state-of-the-art techniques and feature engineering workflows.
  

• Cross-functional Collaboration: Work with product and data science teams to bridge the gap between AI research and business solutions.
  

  
  

• Education: Master’s or PhD in Computer Science, Machine Learning, AI, or a related quantitative field.
  

• GenAI Expertise: Hands-on experience with Transformer architectures, Diffusion models, GANs, and VAEs.
  

• Python Mastery: Expert-level Python with deep knowledge of PyTorch, TensorFlow, and Hugging Face.
  

• LLM Frameworks: Proficiency in model versioning, fine-tuning strategies, and deployment using LangChain.
  

• Cloud & MLOps: Knowledge of AWS/Azure/GCP ML services and experience with model monitoring and production scaling.
  

• Mathematics: Strong foundation in statistics, linear algebra, and optimization.
  

• Problem Solving: Proven ability to deploy models in production and navigate ambiguous project requirements.