Senior Machine Learning Engineer - AI

As a Machine Learning Engineer specializing in Generative AI, NLP, and MLOps, your role will involve building and scaling a multi-model AI platform on AWS infrastructure. You will work on LLM pipelines, NLP systems, ML training infrastructure, and MLOps workflows deployed on Kubernetes (AWS EKS). Collaboration with cloud engineers and platform teams to develop scalable AI-powered applications using AWS Bedrock and transformer-based models is essential. Key Responsibilities: - Build and optimize machine learning training pipelines for NLP and Generative AI models. - Develop synthetic data generation and data augmentation workflows to enhance training datasets. - Manage ML experiment tracking, model registry, and lifecycle management using MLflow. - Deploy and manage GPU-based ML training workloads on Kubernetes / AWS EKS. - Work with Large Language Models (LLMs) and task-specific ML models. - Build and integrate Generative AI workflows using AWS Bedrock and other LLM platforms. - Contribute to model serving infrastructure and inference APIs for multi-model AI platforms. - Ensure reproducibility, monitoring, and observability of ML experiments and production models. Qualifications Required: - Strong hands-on experience in Natural Language Processing (NLP). - Experience with Transformer-based models and Large Language Models (LLMs). - Experience in document analysis and working with Generative AI workflows. - Strong proficiency in Python. - Hands-on experience with ML frameworks like MLflow. - Experience with Kubernetes (preferably AWS EKS) and GPU-based ML workloads. - Familiarity with Docker containers. - Experience designing ML training pipelines and dataset preparation with data versioning. - Experience with synthetic data generation or data augmentation is preferred. - Experience working with AWS cloud services, AWS Lambda, and API-based ML services. - Knowledge of AWS Bedrock for Generative AI or LLM applications. - Experience with LLM platforms such as AWS Bedrock or OpenAI APIs. - Experience with distributed training, Kubernetes Jobs, and building model serving APIs using FastAPI or TorchServe. - Experience in designing scalable AI platforms or multi-model ML systems. In summary, this role requires at least 4+ years of experience in Machine Learning Engineering, production experience with MLflow, and experience deploying LLMs or Generative AI systems in production. As a Machine Learning Engineer specializing in Generative AI, NLP, and MLOps, your role will involve building and scaling a multi-model AI platform on AWS infrastructure. You will work on LLM pipelines, NLP systems, ML training infrastructure, and MLOps workflows deployed on Kubernetes (AWS EKS). Collaboration with cloud engineers and platform teams to develop scalable AI-powered applications using AWS Bedrock and transformer-based models is essential. Key Responsibilities: - Build and optimize machine learning training pipelines for NLP and Generative AI models. - Develop synthetic data generation and data augmentation workflows to enhance training datasets. - Manage ML experiment tracking, model registry, and lifecycle management using MLflow. - Deploy and manage GPU-based ML training workloads on Kubernetes / AWS EKS. - Work with Large Language Models (LLMs) and task-specific ML models. - Build and integrate Generative AI workflows using AWS Bedrock and other LLM platforms. - Contribute to model serving infrastructure and inference APIs for multi-model AI platforms. - Ensure reproducibility, monitoring, and observability of ML experiments and production models. Qualifications Required: - Strong hands-on experience in Natural Language Processing (NLP). - Experience with Transformer-based models and Large Language Models (LLMs). - Experience in document analysis and working with Generative AI workflows. - Strong proficiency in Python. - Hands-on experience with ML frameworks like MLflow. - Experience with Kubernetes (preferably AWS EKS) and GPU-based ML workloads. - Familiarity with Docker containers. - Experience designing ML training pipelines and dataset preparation with data versioning. - Experience with synthetic data generation or data augmentation is preferred. - Experience working with AWS cloud services, AWS Lambda, and API-based ML services. - Knowledge of AWS Bedrock for Generative AI or LLM applications. - Experience with LLM platforms such as AWS Bedrock or OpenAI APIs. - Experience with distributed training, Kubernetes Jobs, and building model serving APIs using FastAPI or TorchServe. - Experience in designing scalable AI platforms or multi-model ML systems. In summary, this role requires at least 4+ years of experience in Machine Learning Engineering, production experience with MLflow, and experience deploying LLMs or Generative AI systems in production.