Senior AI Engineer

Job Description

We are seeking an experienced Senior AI Engineer to design, develop, and deploy intelligent systems with a strong focus on Agentic AI, Retrieval-Augmented Generation (RAG), and large-scale Generative AI architectures. The role involves building production-ready AI solutions, optimizing model performance, and collaborating with cross-functional teams to deliver scalable, high-impact AI systems.

Key Responsibilities:

• Design, develop, and deploy Agentic AI systems and Generative AI solutions
  
• Build and implement RAG and Information Retrieval pipelines for real-world use cases
  
• Develop and integrate Large Language Models into production systems
  
• Perform model evaluation, experimentation, and continuous improvement
  
• Optimize model performance, scalability, and inference efficiency
  
• Develop AI solutions using Python and modern ML/AI frameworks
  
• Work with cloud platforms to deploy and manage AI workloads
  
• Collaborate with engineering and product teams to translate requirements into AI-driven solutions
  
• Apply prompt engineering techniques to improve LLM outputs
  
• Maintain documentation and best practices for AI model development and deployment
  

Requirements

• 5–8 years of experience in AI/ML engineering or related roles
  
• Strong hands-on experience with Agentic AI, RAG, IR, and LLM-based systems
  
• Proficiency in Python and ML/AI frameworks such as TensorFlow, PyTorch, and Scikit-learn
  
• Experience with cloud platforms, preferably AWS and GCP
  
• Strong understanding of model development, evaluation, and optimization
  
• Ability to work independently and deliver under tight timelines
  
• Immediate to 20 days notice period preferred
  
• Candidates from JNTU Hyderabad to be avoided as per hiring guidelines
  

Requirements
Agentic AI, Python, Retrieval-Augmented Generation (RAG), Information Retrieval (IR), Large Language Models (LLMs), Generative AI, Prompt Engineering, Model Evaluation & Experimentation, TensorFlow, PyTorch, Scikit-learn, AutoML, AWS (SageMaker, EC2, S3), GCP, Cloud-Based Model Deployment, Performance Optimization