GenAI Engineer

We are looking for aGenerative AI Engineerwith 3+ years of hands-on experience in building AI-driven applications. The ideal candidate will have strong expertise inmachine learning, deep learning, and large language models (LLMs), with a passion for applying GenAI to solve real-world problems. You will collaborate with product, data science, and engineering teams to design, fine-tune, and deploy generative AI models at scale.

• Design, fine-tune, and deployLLMs and other generative AI modelsfor production use cases.
  

  
  

• Work withtransformer architectures(GPT, BERT, LLaMA, etc.) for text, image, or multimodal tasks.
  

  
  

• Buildend-to-end pipelinesfor training, inference, and evaluation of AI models.
  

  
  

• Implementprompt engineering, RAG (Retrieval-Augmented Generation), and model optimizationfor improved performance.
  

  
  

• Integrate GenAI capabilities intoweb, mobile, or enterprise applications.
  

  
  

• Leverage frameworks such asLangChain, Hugging Face, TensorFlow, PyTorch.
  

  
  

• Collaborate with backend/frontend teams to developAPIs and servicesthat serve AI models.
  

  
  

• Ensure AI solutions meetscalability, latency, and securityrequirements.
  

  
  

• Research and stay updated on the latest advancements inGenerative AI, LLMOps, and ML infrastructure.
  

  
  

• Bachelor’s or Master’s in Computer Science, AI/ML, Data Science, or related field.
  

  
  

• 4+ years of experiencein machine learning, NLP, or AI development.
  

  
  

• Proficiency inPythonand libraries likePyTorch, TensorFlow, Hugging Face Transformers.
  

  
  

• Solid understanding ofLLMs, embeddings, vector databases (Pinecone, Qdrant, Weaviate, FAISS).
  

  
  

• Experience withRAG pipelines, fine-tuning, or prompt engineering.
  

  
  

• Familiarity withcloud platforms(AWS, GCP, Azure) for ML deployment.
  

  
  

• Strong knowledge ofAPIs, microservices, and containerization (Docker, Kubernetes).
  

  
  

• Experience withLangChain / LangGraph LlamaIndexfor AI application orchestration.
  

  
  

• Knowledge ofMLOps / LLMOps pipelines.
  

  
  

• Exposure tomultimodal AI(text, image, speech).
  

  
  

• Hands-on experience withvector search optimizations.
  

  
  

• Contributions toopen-source AI projects.