AI Engineer

About the job

• Develop, fine-tune, and integrate LLMs (GPT, LLaMA, Claude, etc.) into enterprise applications. • Implement prompt engineering techniques, embeddings, and RAG (Retrieval Augmented Generation) pipelines. • Understanding of building Agentic AI applications using Langgraph, etc. • Build and maintain APIs, microservices, and front-end / back-end integrations for GenAI applications. • Work with vector databases (Pinecone, FAISS, Weaviate, Milvus) to enable semantic search. • Deploy solutions on cloud AI platforms (Azure OpenAI, AWS Bedrock, GCP Vertex AI). • Optimize model performance, latency, and scalability for real-world usage. • Collaborate with data scientists and solution architects to deliver PoCs and production-ready applications. • Ensure AI solutions follow responsible AI, data privacy, and security guidelines. Required Skills: • Strong programming skills in Python (preferred), with experience in Java APIs, microservices, Flask / FastAPI / Django. • Knowledge of vector embeddings and vector databases. • Experience with LangChain, LlamaIndex, or similar orchestration frameworks. • Familiarity with Docker, Kubernetes, and MLOps practices for deploying AI models. • Experience working with cloud-based AI services (Azure, AWS, or GCP). • Strong problem-solving and debugging skills. • Good to have experience with PyTorch, TensorFlow, Hugging Face Transformers.

Requirements

• LLMs
• GenAI
• Microservices
• Python
• MLOps

Preferred Technologies

• LLMs
• GenAI
• Microservices
• Python
• MLOps