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Full Stack AI Engineer

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Onsite

Full Stack AI Engineer

Daten Technology Solutions

Full Stack AI Engineer

Skills

ReactVueFlaskPythonJavaAmazon EC2AWS LambdaMachine LearningAIAWSFastAPI

4 months ago

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Important information


Contract type:

Permanent

Location:

Noida, Uttar Pradesh, India

Work mode:

Onsite

Published on:

14 October 2026

What they need


Job highlights

  • Shift timings: 2-11 PM . Disclaimer: This job posting has been aggregated from external source
  • Experience building AI-integrated web applications.
  • . Read,analyze,and interface with existing Java-based services or codebases as required. Frontend Development
  • Required Skills . AI / ML

Full Stack AI Engineer

  • Total Experience : 5 to 10 Years
  • No of Openings : 1
  • Type : On site
  • Location : Noida [India]

About the Job

We are seeking a Full Stack AI Engineer to design, build, and deploy next-generation agentic AI applications leveraging Large Language Models (LLMs), multi-agent orchestration frameworks, and cloud-native AWS services. This role requires deep expertise in Python-based AI backends, LLM fine-tuning and integration, modern frontend frameworks, and scalable AWS deployments.

The ideal candidate will operate across the entire AI application lifecycle from model fine-tuning and agent workflow design to frontend development and production-grade cloud deployment.

Key Responsibilities

AI / Agentic Systems

  • Design and implement multi-agent pipeline systems for complex reasoning, orchestration, and task execution.

  • Develop and manage agentic workflows using frameworks such as LangChain, AWS Strands, or similar .

  • Fine-tune and adapt LLMs using custom enterprise data on AWS Bedrock and/or OpenAI platforms.

  • Implement advanced prompt engineering strategies for task decomposition, tool calling, and agent collaboration.

Backend Engineering

  • Build and maintain Python-based backend APIs using Flask or FastAPI .

  • Integrate LLM inference, vector search, memory, and agent logic into scalable backend services.

  • Develop ML pipelines for data preprocessing, embedding generation, evaluation, and inference.

  • Read, analyze, and interface with existing Java-based services or codebases as required.

Frontend Development

  • Develop intuitive and responsive web interfaces using React.js or Vue.js .

  • Integrate frontend components with AI-driven backend APIs for real-time inference and agent interactions.

  • Collaborate with UX and product teams to translate AI workflows into usable customer experiences.

Cloud Deployment

  • Deploy and operate full-stack AI applications on

  • AWS

  • , leveraging:

  • AWS Bedrock for LLM access and fine-tuning

  • S3 for data and artifact storage

  • Lambda for serverless workflows

  • EC2 for scalable compute workloads

  • Implement secure, scalable, and cost-optimized cloud architectures.

  • Support CI/CD pipelines and production monitoring for AI services.

Requirements

Required Skills

AI / ML

  • Hands-on experience with LLM fine-tuning and inference (AWS Bedrock, OpenAI).

  • Strong expertise in AI agent development and prompt engineering .

  • Experience building production-grade GenAI applications .

Agent Frameworks

  • Practical experience with LangChain, AWS Strands , or equivalent agent orchestration frameworks.

Backend Development

  • Expert-level Python skills.

  • Experience with Flask/FastAPI , ML pipelines, and large-scale data processing.

  • Ability to read and analyze Java code for integration or enhancement.

Frontend

  • Strong working knowledge of React.js or Vue.js .

  • Experience building AI-integrated web applications.

Cloud (AWS)

  • Hands-on experience with AWS Bedrock, S3, Lambda, EC2 .

  • Understanding of cloud security, scalability, and performance best practices.

Nice-to-Have Skills

  • Deep experience in GenAI application development beyond PoCs.

  • Exposure to multi-agent system architectures in production environments.

  • Experience designing agentic workflows for enterprise-scale use cases.

  • Familiarity with vector databases, embeddings, and retrieval-augmented generation (RAG).

  • MLOps and model evaluation experience.

Shift timings: 2-11 PM

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

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