Description

You will design, develop, and deploy generative AI and RAG architectures for large-scale machine learning applications.

This role is on-site.

Responsibilities

• Build and fine-tune LLMs using OpenAI GPT and Anthropic Claude.
• Design and implement RAG pipelines utilizing 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 ensure low-latency API performance.

Required Skills

• 5+ years of experience in machine learning engineering.
• Proficiency in Python.
• Hands-on experience with PyTorch, TensorFlow, and Hugging Face Transformers.
• Direct experience with OpenAI GPT and Anthropic Claude models.
• Strong understanding of RAG architectures and vector databases (Pinecone, Weaviate, OpenSearch).
• Experience with API development using FastAPI, Flask, or Django.
• Proficiency in containerization using Docker, AWS ECS, and Kubernetes.
• Experience deploying models on AWS (SageMaker preferred), GCP Vertex AI, or Azure ML.
• Ability to work with SQL or NoSQL databases for data extraction and preprocessing.

Preferred Skills

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

Key Skills

Education

Any Graduate