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: 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