Python Engineer - AI/ML & LLM Specialist

Seeking a hands-on Python Engineer with strong expertise in AI/ML, NLP, and Large Language Models (LLMs) to build and deploy production-grade AI applications. The role involves designing scalable LLM systems, developing RAG pipelines, fine-tuning transformer models, and building end-to-end ML infrastructure for real-world business applications.

Responsibilities

• Design and build AI/ML systems focused on NLP and LLM-powered applications such as chatbots, summarization, classification, semantic search, and entity extraction.

• Fine-tune and optimize LLMs including LLaMA, Mistral, GPT variants, and transformer-based models using LoRA, PEFT, RLHF, and prompt engineering techniques.

• Build and implement RAG (Retrieval-Augmented Generation) pipelines integrated with vector databases like Pinecone, Weaviate, ChromaDB, and FAISS.

• Develop scalable ML pipelines covering preprocessing, model training, evaluation, deployment, monitoring, and retraining.

• Optimize model inference using quantization, pruning, batching, and performance tuning techniques.

• Build REST APIs and microservices using FastAPI or Flask for AI model deployment.

• Collaborate with product, engineering, and data teams to deliver scalable AI solutions.

• Maintain experiment tracking, model versioning, and MLOps workflows.

• Stay updated with emerging LLM technologies, frameworks, and AI tooling.

  Required Skills

• Python

• PyTorch

• NLP

• LLMs

• Generative AI

• Hugging Face

• LangChain

• LlamaIndex

• RAG Pipelines

• Prompt Engineering

• FastAPI

• Flask

• SQL

• Pandas

• NumPy

• Machine Learning

• Transformer Models

• Model Deployment

• Semantic Search

• Conversational AI

  Preferred Skills

• Experience with vector databases including Pinecone, Weaviate, ChromaDB, and FAISS.

• Familiarity with AWS SageMaker, GCP Vertex AI, or Azure ML.

• Experience with MLOps tools such as MLflow, Weights Biases, and DVC.

• Knowledge of distributed computing frameworks like Spark, Ray, or Dask.

• Understanding of quantization, pruning, distillation, and inference optimization techniques.

• Contributions to open-source AI/ML projects are a plus.

• Education: Bachelor's degree in Computer Science, Engineering, Mathematics, or a related field. Equivalent practical experience, open-source work, or self-driven AI/ML projects will also be considered.

Disclaimer: This job posting and Location has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.