Job Title

Machine Learning Engineer – Generative AI / NLP / AWS Bedrock Location: Remote Budget: Up to ₹1 LPM Working Hours: Minimum overlap with 8:00 AM – 4:00 PM EST Experience: 4+ Years About the Role We are looking for a Machine Learning Engineer with strong expertise in Generative AI, NLP, and MLOps to help build and scale a multi-model AI platform running on AWS infrastructure . The ideal candidate will work on LLM pipelines, NLP systems, ML training infrastructure, and MLOps workflows deployed on Kubernetes (AWS EKS) . You will collaborate closely with cloud engineers and platform teams to develop scalable AI-powered applications using AWS Bedrock and transformer-based models . This role is ideal for someone passionate about Large Language Models, generative AI systems, and production-grade ML pipelines . Key Responsibilities Design and develop NLP pipelines for: Text processing Document understanding Semantic search Text summarization 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. Required Skills Machine Learning & NLP Strong hands-on experience in Natural Language Processing (NLP) Experience with Transformer-based models and Large Language Models (LLMs) Experience with: Text processing Document analysis Embeddings Semantic search Summarization Experience working with Generative AI workflows Programming Strong proficiency in Python Experience with ML frameworks: PyTorch TensorFlow Hugging Face Transformers MLOps Hands-on experience with MLflow , including: Experiment tracking Model registry Model lifecycle management Infrastructure Experience with Kubernetes (preferably AWS EKS) Experience running GPU-based ML workloads Familiarity with Docker containers Data & Training Pipelines Experience designing ML training pipelines Experience with dataset preparation and data versioning Understanding of experiment reproducibility Experience with synthetic data generation or data augmentation (preferred) Cloud Platforms Experience working with AWS cloud services Familiarity with: Amazon S3 AWS Lambda API-based ML services Experience with AWS Bedrock for Generative AI or LLM applications Nice to Have Experience with LLM platforms such as AWS Bedrock or OpenAI APIs Experience with distributed training or Kubernetes Jobs Experience building model serving APIs using FastAPI or TorchServe Experience designing scalable AI platforms or multi-model ML systems Experience Requirements 4+ years of experience in Machine Learning Engineering 2+ years of hands-on NLP development Production experience with MLflow Experience deploying LLMs or Generative AI systems in production How to Apply Interested candidates can share their CV at Subject Line: Machine Learning Engineer – Generative AI