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AI/ML Engineering
Python Engineer – AI/ML & LLM Specialist
Work Experience5+ Years
LocationRemote / Hybrid – EST Hours
EmploymentFull Time
Role Overview
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
PythonPyTorchNLPLLMsGenerative AIHugging FaceLangChainLlamaIndexRAG PipelinesPrompt EngineeringFastAPIFlaskSQLPandasNumPyMachine LearningTransformer ModelsModel DeploymentSemantic SearchConversational 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.