considerations. " 

Job Description & Summary: We are seeking a highly skilled and innovative GenAI Engineer to join our dynamic team. The ideal candidate will be responsible for designing, developing, and deploying scalable Generative AI solutions using state-of-the-art large language models (LLMs) and transformer architectures. This includes building intelligent applications, orchestrating model workflows, and integrating GenAI capabilities into enterprise systems. 

This role demands deep expertise in Python, PyTorch, and Hugging Face Transformers, along with hands-on experience in deploying solutions on Azure, AWS, or GCP. The candidate should be proficient in using orchestration frameworks like LangChain, developing APIs with FastAPI or Flask, and managing ML pipelines using tools such as MLflow or Weights & Biases. Familiarity with CI/CD practices for ML, including platforms like Azure ML or SageMaker Pipelines, is essential. 

Responsibilities: 

• Design, build, and deploy generative AI solutions using LLMs such as OpenAI, Anthropic, Mistral, or open-source models (e.g., LLaMA, Falcon). 

• Fine-tune and customize foundation models using domain-specific datasets and techniques  

• Develop and optimize prompt engineering strategies to drive accurate and context-aware model responses. 

• Implement model pipelines using Python and ML frameworks such as PyTorch, Hugging Face Transformers, or LangChain. 

• Agentic AI implementation expertise using Crew.ai or Lang chain 

• Collaborate with data engineers and MLOps teams to productionize GenAI models on cloud platforms (Azure/AWS/GCP). 

• Knowledge on Azure/GCP/AWS AI platform like Azure AI Foundry or GCP Vertex  

• Ensure robustness, scalability, and compliance of AI models in deployment environments. 

• Good to have experience in finetuning models 

• Good to have experience in SLM 

• Integrate GenAI into enterprise applications via APIs or custom interfaces. 

• Evaluate model performance using quantitative and qualitative metrics, and improve outputs through iterative experimentation. 

• Keep up to date with the latest research in GenAI, foundation models, and relevant open-source tools. 

Mandatory skill sets: 

• Generative AI (LLMs, Transformers) 

• Python, PyTorch, Hugging Face Transformers 

• Azure/AWS/GCP cloud platforms 

• LangChain/Langgraph or similar orchestration frameworks 

• REST APIs, FastAPI, Flask 

• ML pipeline tools (MLflow, Weights & Biases) 

• Git, CI/CD for ML (e.g., Azure ML, SageMaker pipelines) 

Preferred skill sets: 

• RAG (Retrieval-Augmented Generation) 

• Vector DBs (FAISS, Pinecone, Weaviate) 

• Streamlit/Gradio for prototyping 

• Docker, Kubernetes (for model deployment) 

• Data preprocessing & feature engineering 

• NLP libraries: spaCy, NLTK, Transformers 

Years of experience required: 

4 to 7 years 

Education qualification: 

B.E/B.tech/M.tech/MCA