Python Developer – LLM / Generative AI

About the Role

Python Developer with hands-on expertise in LLMs (Large Language Models)

Key Responsibilities

• Develop, integrate, and optimize

  LLM-based applications

  using Python.
• Build

  RAG (Retrieval Augmented Generation)

  pipelines, embedding workflows, and prompt-based systems.
• Implement

  model fine-tuning

  , evaluation, and deployment of open-source and proprietary LLMs.
• Work with

  Hugging Face Transformers

  , LangChain, LlamaIndex, or similar frameworks.
• Create scalable

  REST APIs / microservices

  around AI/ML models.
• Develop data ingestion, preprocessing, and vectorization pipelines.
• Integrate vector databases like

  Pinecone, Weaviate, FAISS, Milvus

  , etc.
• Deploy LLM applications on

  AWS / Azure / GCP

  using containerized environments.
• Collaborate with product, data, and engineering teams to deliver end-to-end AI solutions.
• Perform unit testing, benchmarking, and optimization of model performance and latency.

Required Skills

• Strong experience in

  Python

  (FastAPI, Flask, Django preferred).
• Hands-on experience with

  LLMs / Generative AI

  , such as GPT, LLaMA, Mistral, Falcon, etc.
• Knowledge of

  Hugging Face

  , LangChain, LlamaIndex, or similar toolkits.
• Strong skills in

  NLP

  , embeddings, tokenization, and transformer architectures.
• Experience in

  model fine-tuning

  , quantization, or inference optimization.
• Good understanding of

  Vector DBs

  (FAISS, Pinecone, Milvus, Chroma).
• Experience with

  cloud platforms

  (AWS/Azure/GCP) and containerization (Docker/Kubernetes).
• Strong understanding of

  REST APIs

  , microservices, Git/GitHub, and CI/CD workflows.
• Solid problem-solving, debugging, and algorithmic thinking.

Good to Have

• Experience with

  LLMOps

  or AI model deployment automation.
• Knowledge of

  data engineering pipelines

  .
• Experience with

  GPU optimization

  or distributed training.
• Familiarity with

  OpenAI, Anthropic, Azure OpenAI, Vertex AI

  , etc.
• Exposure to

  MLOps tools

  like MLflow, Weights & Biases, or Kubeflow.

Education

• Bachelors or Masters degree in Computer Science, Engineering, AI/ML, or equivalent experience.