Role Overview
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.
Key Responsibilities
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.
Technical Skills & Qualifications
Core Requirements
Education: Masters 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.
Infrastructure & Math
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.
Role OverviewWe 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 adv...
Role Overview
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.
Key Responsibilities
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.
Technical Skills & Qualifications
Core Requirements
Education: Masters 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.
Infrastructure & Math
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.
View more
View less