AI Engineer Full Stack GenAI

Only immediate joiner to 30days of notice will be considered Not more than that.

AI Engineer – Full Stack GenAI

Location: Viman Nagar, Pune
Experience: 4+ Years
Work Mode: Hybrid
Working Hours: 11:00 AM – 8:00 PM

About the Role

We are looking for an AI Engineer with strong Full-Stack development experience to build, deploy, and scale production-grade Generative AI applications. The ideal candidate should be hands-on with Next.js, TypeScript, Python, LLM applications, RAG, cloud platforms, and LLMOps.

Key Responsibilities

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  Build and optimize full-stack GenAI applications using Next.js, TypeScript, and Python.

  
  


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  Design, develop, and deploy production-grade AI systems, including Retrieval-Augmented Generation (RAG) solutions for search and discovery.

  
  


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  Develop and integrate LLM-powered applications for content generation, summarization, metadata enrichment, and other AI use cases.

  
  


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  Work with modern GenAI frameworks such as LangChain, LlamaIndex, DSPy, and Hugging Face Transformers.

  
  


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  Implement advanced RAG pipelines, including prompt engineering, chunking strategies, embeddings, and vector database integration.

  
  


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  Deploy and manage LLM applications using cloud-based services such as Azure OpenAI or AWS Bedrock.

  
  


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  Implement LLMOps and observability to monitor latency, cost, accuracy, hallucination, toxicity, and data drift.

  
  


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  Collaborate across the AI and engineering stack to build scalable, reliable, and production-ready solutions.

  
  

Mandatory Requirements

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  4+ years of relevant professional experience in software, AI engineering.

  
  


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  Strong Full-Stack development experience with hands-on expertise in:

  
  
  
  
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    Next.js

    
    
  
  
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    TypeScript

    
    
  
  
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    Python

    
    
  
  
  
  


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  Demonstrable experience building and productionizing LLM/GenAI applications.

  
  


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  Strong practical knowledge of RAG architecture, including:

  
  
  
  
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    Prompt engineering

    
    
  
  
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    Chunking strategies

    
    
  
  
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    Embeddings

    
    
  
  
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    Vector databases such as Pinecone, Weaviate, or Milvus

    
    
  
  
  
  


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  Hands-on experience with at least one modern GenAI/LLM framework such as LangChain, LlamaIndex, DSPy, or Hugging Face Transformers.

  
  


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  Experience with managed LLM services such as Azure OpenAI Service or AWS Bedrock.

  
  


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  Strong foundational knowledge of Azure or AWS cloud services.

  
  


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  Experience with containerization and deployment tools such as Docker and CI/CD pipelines (GitHub Actions, Argo, or similar).

  
  


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  Experience deploying and managing production-grade AI/LLM solutions.

  
  


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  Understanding of LLMOps, observability, and monitoring for AI applications.

  
  

Nice-to-Have Skills

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  Experience with agentic AI workflows using tools such as AutoGen or CrewAI.

  
  


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  Exposure to multimodal AI models involving text, images, or other data types.

  
  


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  Knowledge of advanced LLM fine-tuning techniques such as LoRA or QLoRA.

  
  


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  Experience with AKS/EKS, serverless functions, and cloud storage.

  
  


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  Strong SQL skills, particularly ClickHouse.

  
  


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  Experience with inference cost optimization and AI application performance tuning.

  
  


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  Experience with monitoring tools such as OpenTelemetry and Prometheus.

  
  


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  Knowledge of model registries and MLOps best practices.