Senior machine learning engineer

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:

Py Torch

Tensor Flow

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 Open AI APIs Experience with distributed training or Kubernetes Jobs Experience building model serving APIs using Fast API or Torch Serve 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