Description

You will design, develop, and deploy advanced AI/ML models with a focus on Generative AI and RAG architectures.

Responsibilities

• Build and fine-tune large language models using OpenAI GPT or Anthropic Claude.
• Design and implement RAG pipelines leveraging vector databases like Pinecone, Weaviate, or OpenSearch.
• Architect, train, and deploy end-to-end ML pipelines for NLP and multimodal AI solutions.
• Deploy models in production using AWS SageMaker or ECS and manage CI/CD with Docker, Kubernetes, and MLflow.
• Optimize embeddings for semantic search and document retrieval tasks.

Required Skills

• 5+ years of experience in machine learning engineering.
• Proficiency in Python.
• Hands-on experience with LLMs and fine-tuning techniques.
• Deep understanding of RAG architectures and vector databases.
• Experience with PyTorch, TensorFlow, and Hugging Face Transformers.
• API development using FastAPI, Flask, or Django.
• Containerization and orchestration using Docker and Kubernetes.
• Cloud deployment experience with AWS (SageMaker, ECS), GCP Vertex AI, or Azure ML.
• Proficiency with SQL or NoSQL databases.

Preferred Skills

• Experience building conversational AI systems or chatbots.
• Knowledge of NLP techniques like sentiment analysis, topic modeling, or summarization.
• Familiarity with serverless architectures such as AWS Lambda.

Key Skills

Education

Any Graduate