Generative AI Engineer

Overview

xtagservices is seeking experienced Generative AI Engineers to join their teams in Hyderabad, Chennai, Bangalore, and Pune. This full-time, onsite role is for professionals with hands-on experience in building and deploying enterprise-grade Generative AI solutions, including work with large language models, vector databases, and AI orchestration frameworks.

Core Responsibilities

• Design and develop generative AI solutions aligned with enterprise requirements.
• Build retrieval-augmented generation (RAG) applications leveraging large language models (LLMs) and vector databases.
• Implement and manage AI agent frameworks with orchestration capabilities.
• Conduct model fine-tuning, experimentation, deployment, and continuous monitoring to ensure reliable performance.
• Ensure solutions are scalable, secure, and compliant with enterprise standards.

Candidate Requirements

• 5 to 11 years of relevant industry experience, specifically with generative AI technologies.
• Proven expertise in developing and deploying generative AI models, including fine-tuning and production-grade implementation.
• Strong understanding of AI/ML systems architecture and operationalization in production.
• Familiarity with state-of-the-art models such as BERT, LLaMA, and advanced frameworks for AI agent development.
• Hands-on with machine learning toolkits and ecosystems including TensorFlow, PyTorch, and MLflow.

Additional Details

• Number of Openings: 6 positions available.
• Competitive salary budget supporting up to four times the maximum stated range ($42,200 to $67,500 annually).
• Work location options include Hyderabad, Chennai, Bangalore, and Pune on an onsite basis.

Skills and Knowledge Areas

• Large language models (LLMs) and prompt engineering methodologies.
• Retrieval-Augmented Generation (RAG) pipeline design and implementation.
• AI agent framework development and orchestration techniques.
• Vector database management, including tools such as Pinecone, Weaviate, and Qdrant.
• Model experimentation, fine-tuning techniques including PEFT, LoRA, and reinforcement learning from human feedback (RLHF).
• Deployment and monitoring frameworks with containerization technologies like Docker and Kubernetes.
• Strong focus on scalable AI system deployment, security, and compliance.