Remote
Full-Time
India
Skills
Amazon Web Services (AWS)
Python (Programming Language)
PyTorch
Large Language Models (LLM)
TensorFlow
AWS SageMaker
About the Role
AI/ML Engineer — NLP, LLMs & RAG Systems
Description
We’re seeking a hands-on AI/ML Engineer with deep expertise in large language models, retrieval-augmented generation (RAG), and cloud-native ML development on AWS. You'll be a key driver in building scalable, intelligent learning systems powered by cutting-edge AI and robust AWS infrastructure.
If you’re passionate about combining NLP, deep learning, and real-world application at scale—this is the role for you.
Core Skills & Technologies
LLM Ecosystem & APIs
OpenAI, Anthropic, Cohere
Hugging Face Transformers
LangChain, LlamaIndex (RAG orchestration)
Vector Databases & Indexing
FAISS, Pinecone, Weaviate
AWS-Native & ML Tooling
Amazon SageMaker (training, deployment, pipelines)
AWS Lambda (event-driven workflows)
Amazon Bedrock (foundation model access)
Amazon S3 (data lakes, model storage)
AWS Step Functions (workflow orchestration)
AWS API Gateway & IAM (secure ML endpoints)
CloudWatch, Athena, DynamoDB (monitoring, analytics, structured storage)
Languages & ML Frameworks
Python (primary), PyTorch, TensorFlow
NLP, RAG systems, embeddings, prompt engineering
What You’ll Do
Model Development & Tuning
Fine-tune and deploy LLMs and custom models using AWS SageMaker
Build RAG pipelines with LlamaIndex/LangChain and vector search engines
Scalable AI Infrastructure
Architect distributed model training and inference pipelines on AWS
Design secure, efficient ML APIs with Lambda, API Gateway, and IAM
Product Integration
Embed intelligent systems (tutoring agents, recommendation engines) into learning platforms using Bedrock, SageMaker, and AWS-hosted endpoints
Rapid Experimentation
Prototype multimodal and few-shot learning workflows using AWS services
Automate experimentation and A/B testing with Step Functions and SageMaker Pipelines
Data & Impact Analysis
Leverage S3, Athena, and CloudWatch to define metrics and continuously optimize AI performance
Cross-Team Collaboration
Work closely with educators, designers, and engineers to deliver AI features that enhance student learning
Who You Are
Deeply Technical: Strong foundation in machine learning, deep learning, and NLP/LLMs
AWS-Fluent: Extensive experience with AWS ML services (especially SageMaker, Lambda, and Bedrock)
Product-Minded: You care about user experience and turning ML into real-world value
Startup-Savvy: Comfortable with ambiguity, fast iterations, and wearing many hats
Mission-Aligned: Passionate about education, human learning, and AI for good
Bonus Points
Hands-on experience fine-tuning LLMs or building agentic systems using AWS
Open-source contributions in AI/ML or NLP communities
Familiarity with AWS security best practices (IAM, VPC, private endpoints)
Description
We’re seeking a hands-on AI/ML Engineer with deep expertise in large language models, retrieval-augmented generation (RAG), and cloud-native ML development on AWS. You'll be a key driver in building scalable, intelligent learning systems powered by cutting-edge AI and robust AWS infrastructure.
If you’re passionate about combining NLP, deep learning, and real-world application at scale—this is the role for you.
Core Skills & Technologies
LLM Ecosystem & APIs
OpenAI, Anthropic, Cohere
Hugging Face Transformers
LangChain, LlamaIndex (RAG orchestration)
Vector Databases & Indexing
FAISS, Pinecone, Weaviate
AWS-Native & ML Tooling
Amazon SageMaker (training, deployment, pipelines)
AWS Lambda (event-driven workflows)
Amazon Bedrock (foundation model access)
Amazon S3 (data lakes, model storage)
AWS Step Functions (workflow orchestration)
AWS API Gateway & IAM (secure ML endpoints)
CloudWatch, Athena, DynamoDB (monitoring, analytics, structured storage)
Languages & ML Frameworks
Python (primary), PyTorch, TensorFlow
NLP, RAG systems, embeddings, prompt engineering
What You’ll Do
Model Development & Tuning
Fine-tune and deploy LLMs and custom models using AWS SageMaker
Build RAG pipelines with LlamaIndex/LangChain and vector search engines
Scalable AI Infrastructure
Architect distributed model training and inference pipelines on AWS
Design secure, efficient ML APIs with Lambda, API Gateway, and IAM
Product Integration
Embed intelligent systems (tutoring agents, recommendation engines) into learning platforms using Bedrock, SageMaker, and AWS-hosted endpoints
Rapid Experimentation
Prototype multimodal and few-shot learning workflows using AWS services
Automate experimentation and A/B testing with Step Functions and SageMaker Pipelines
Data & Impact Analysis
Leverage S3, Athena, and CloudWatch to define metrics and continuously optimize AI performance
Cross-Team Collaboration
Work closely with educators, designers, and engineers to deliver AI features that enhance student learning
Who You Are
Deeply Technical: Strong foundation in machine learning, deep learning, and NLP/LLMs
AWS-Fluent: Extensive experience with AWS ML services (especially SageMaker, Lambda, and Bedrock)
Product-Minded: You care about user experience and turning ML into real-world value
Startup-Savvy: Comfortable with ambiguity, fast iterations, and wearing many hats
Mission-Aligned: Passionate about education, human learning, and AI for good
Bonus Points
Hands-on experience fine-tuning LLMs or building agentic systems using AWS
Open-source contributions in AI/ML or NLP communities
Familiarity with AWS security best practices (IAM, VPC, private endpoints)
Apply for this position
Application Status
Application Draft
In Progress
Submit Application
Pending
Review Process
Expected within 5-7 days
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