AI/ML Engineer – GenAI & LLM || Immediate Joiners

AI/ML Engineer – GenAI & LLM

Experience:  3+ Years

Location:  Hyderabad / India

Employment Type:  Full-Time

Job Summary

We are looking for an experienced  AI/ML Engineer with strong expertise in Generative AI, Large Language Models (LLMs), and Machine Learning  to design, develop, and deploy intelligent AI solutions.

The ideal candidate will have hands-on experience with  Python, ML frameworks, LLMs, RAG, prompt engineering, vector databases, and AI application development . You will work closely with engineering and product teams to build scalable, production-ready AI solutions.

Key Responsibilities

• Design, develop, and deploy  AI/ML and Generative AI solutions  for real-world business use cases.
• Build applications using  LLMs such as OpenAI, Azure OpenAI, Gemini, Claude, Llama, or similar models .
• Develop  Retrieval-Augmented Generation (RAG)  pipelines using embeddings, vector databases, document processing, and semantic search.
• Implement  prompt engineering, prompt optimization, and LLM evaluation  techniques.
• Develop AI/ML models and pipelines using  Python and frameworks such as PyTorch, TensorFlow, or Scikit-learn .
• Build intelligent applications using  LangChain, LangGraph, LlamaIndex, or equivalent frameworks .
• Work with vector databases such as  FAISS, Pinecone, Weaviate, Milvus, Chroma, or Azure AI Search .
• Develop and integrate  REST APIs and microservices  for AI/ML applications.
• Implement model monitoring, logging, evaluation, and performance optimization for production AI systems.
• Fine-tune or customize LLMs using appropriate techniques such as  fine-tuning, PEFT, LoRA, or prompt-based adaptation  where required.
• Work with structured and unstructured data to develop ML and GenAI solutions.
• Collaborate with Data Scientists, Data Engineers, Software Engineers, and business stakeholders to translate requirements into AI solutions.
• Deploy AI/ML workloads on cloud platforms such as  AWS, Azure, or GCP .
• Implement CI/CD and MLOps practices for reliable deployment and lifecycle management of AI models.