AI Engineer - AWS

Neoqubit Pvt Ltd.; Bengaluru; On-site; INR 1,800,000 - 2,700,000; Full time

Job summary

Neoqubit Pvt Ltd. is seeking an AI Engineer with strong hands-on AWS expertise to design, develop, deploy, and scale AI/ML and generative AI solutions in production environments in Bengaluru. You will work on Python-based LLMs, RAG, and agentic AI, taking AI solutions from POC to production.

Responsibilities include building scalable AI/ML pipelines, deploying on AWS Bedrock/SageMaker, integrating with REST APIs, and enabling MLOps/LLMOps for monitoring and optimization.

Qualifications

• Strong analytical and problem-solving skills.
• Ability to take AI solutions from POC to production.
• Understanding of scalable cloud architecture and production systems.

Responsibilities

• Design, build, and deploy AI/ML and generative AI solutions on AWS.
• Develop LLM-powered applications, AI agents, RAG pipelines, and intelligent automation solutions.
• Build scalable AI/ML pipelines using Python and modern AI frameworks.
• Work with AWS services such as Amazon Bedrock, SageMaker, Lambda, S3, ECS/EKS, EC2, and API Gateway.
• Implement RAG solutions using embedding models and vector databases.
• Develop prompt engineering, model evaluation, fine-tuning, and optimization strategies.
• Build MLOps/LLMOps pipelines for model deployment, monitoring, and continuous improvement.
• Integrate AI models with applications through REST APIs and microservices.
• Optimize AI workloads for performance, scalability, reliability, security, and cost.
• Collaborate with Product, Engineering, Data Science, and DevOps teams to deliver production-ready AI solutions.

Skills

Python

AWS

LLMs

RAG

Agentic AI

LangChain

LlamaIndex

Hugging Face

PyTorch

Docker

Kubernetes

REST APIs

Tools

AWS Bedrock

SageMaker

Docker

Kubernetes

FastAPI

Flask

Job description

We are looking for an AI Engineer with strong hands-on AWS expertise to design, develop, deploy, and scale AI/ML and generative AI solutions in production environments. The ideal candidate will have experience in Python, LLMs, RAG, Agentic AI, AWS AI/ML services, and production-grade deployment, with the ability to take AI solutions from POC to production.

Responsibilities:

• Design, build, and deploy AI/ML and generative AI solutions on AWS.
• Develop LLM-powered applications, AI agents, RAG pipelines, and intelligent automation solutions.
• Build scalable AI/ML pipelines using Python and modern AI frameworks.
• Work with AWS services such as Amazon Bedrock, SageMaker, Lambda, S3, ECS/EKS, EC2, and API Gateway.
• Implement RAG solutions using embedding models and vector databases.
• Develop prompt engineering, model evaluation, fine-tuning, and optimization strategies.
• Build MLOps/LLMOps pipelines for model deployment, monitoring, and continuous improvement.
• Integrate AI models with applications through REST APIs and microservices.
• Optimize AI workloads for performance, scalability, reliability, security, and cost.
• Collaborate with Product, Engineering, Data Science, and DevOps teams to deliver production-ready AI solutions.

Requirements:

• Strong analytical and problem-solving skills.
• Ability to take AI solutions from POC to production.
• Understanding of scalable cloud architecture and production systems.
• Ability to work independently and collaborate across teams.
• Strong communication and documentation skills.
• Key Technology Stack: Python, AWS, Amazon Bedrock, SageMaker, Generative AI, LLMs, RAG, Agentic AI, LangChain, LlamaIndex, Hugging Face, PyTorch, Docker, Kubernetes, REST APIs, Vector Databases, MLOps, and LLMOps.

AI and Generative AI:

• Strong understanding of machine learning, deep learning, NLP, and generative AI.
• Hands-on experience with LLMs, RAG, embeddings, vector databases, and prompt engineering.
• Experience with PyTorch, TensorFlow, Hugging Face, LangChain, or LlamaIndex.
• Understanding of model evaluation, fine-tuning, inference, and optimization. AWS Cloud
• Hands-on experience with Amazon Bedrock and/or SageMaker.
• Experience with AWS Lambda, S3, EC2, ECS/EKS, API Gateway, Step Functions, and CloudWatch.
• Knowledge of IAM, AWS Glue, Athena, and EventBridge is an advantage.

Programming and Engineering:

• Strong proficiency in Python.
• Experience with FastAPI, Flask, or similar frameworks.
• Strong understanding of REST APIs, microservices, Git, Docker, and CI/CD.
• Experience with SQL/NoSQL databases and vector databases such as Pinecone, Weaviate, Milvus, OpenSearch, or pgvector.

Good to Have:

• Experience building GenAI applications using Amazon Bedrock.
• Hands-on experience with Agentic AI, AI agents, and tool/function calling.
• Experience with MLOps/LLMOps, Kubernetes, Terraform, or CloudFormation.
• AWS certifications in machine learning or solutions architecture.
• Knowledge of AI security, data privacy, model governance, and responsible AI.
• Experience in a startup or fast-paced product environment.