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 OpenAI APIs

Experience with  distributed training or Kubernetes Jobs
Experience building  model serving APIs using FastAPI 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  priyan
Subject Line:
Machine Learning Engineer – Generative AI