AI Engineer - LLM/RAG

Key Responsibilities :

• Design, develop, and deploy AI-driven applications and intelligent software solutions.

• Build and optimize machine learning and deep learning models for production environments.

• Develop applications leveraging Large Language Models (LLMs), Generative AI, and Natural Language Processing (NLP).

• Fine-tune foundation models using domain-specific datasets and implement Retrieval-Augmented Generation (RAG) pipelines.

• Develop AI services and RESTful APIs for seamless integration with enterprise applications.

• Build scalable backend systems to support AI inference and model serving.

• Design data pipelines for model training, evaluation, and continuous improvement.

• Implement prompt engineering techniques to improve LLM performance and response quality.

• Optimize model accuracy, latency, scalability, and infrastructure utilization.

• Collaborate with software engineers, product managers, and data scientists throughout the product lifecycle.

• Deploy AI models using containerization and orchestration technologies.

• Implement MLOps practices for automated model training, deployment, monitoring, and version control.

• Evaluate emerging AI technologies and recommend suitable solutions for business use cases.

• Ensure AI applications meet security, governance, and responsible AI standards.

• Troubleshoot production issues and continuously improve system performance.

Required Skills :

• Strong programming skills in Python.

• Experience with machine learning frameworks such as PyTorch, TensorFlow, or Scikit-learn.

• Hands-on experience with Large Language Models (LLMs) and Generative AI technologies.

• Strong understanding of NLP, Transformers, Embeddings, and Vector Databases.

• Experience building RAG-based applications.

• Knowledge of prompt engineering and LLM optimization techniques.

• Experience with LangChain, LangGraph, LlamaIndex, or similar AI orchestration frameworks.

• Experience developing REST APIs using FastAPI or Flask.

• Strong understanding of data structures, algorithms, and software engineering principles.

• Experience with SQL and NoSQL databases.

• Familiarity with Docker, Kubernetes, and containerized deployments.

• Experience with cloud platforms such as AWS, Azure, or Google Cloud Platform.