We are seeking a highly motivated Generative AI / LLMOps Engineer with hands-on experience in building, deploying, monitoring, and optimizing Large Language Model (LLM) applications in production environments. The ideal candidate should have strong expertise in Generative AI, LLMOps, RAG frameworks, cloud platforms, and MLOps practices.

The role involves developing scalable AI solutions, managing LLM lifecycles, and ensuring reliable deployment and monitoring of enterprise-grade GenAI applications.

Design, develop, deploy, and maintain Generative AI applications using LLMs.

Build and operationalize LLM pipelines across development, testing, and production environments.

Implement LLMOps best practices for model deployment, monitoring, evaluation, governance, and lifecycle management.

Develop Retrieval-Augmented Generation (RAG) solutions using enterprise knowledge repositories.

Integrate LLMs with APIs, databases, business applications, and cloud services.

Monitor model performance, latency, cost optimization, hallucinations, and model drift.

Create automated CI/CD pipelines for AI model deployment and versioning.

Collaborate with Data Scientists, ML Engineers, and Cloud Teams to deliver scalable AI solutions.

Ensure security, compliance, observability, and responsible AI practices across GenAI platforms.

Docker & Kubernetes

CI/CD Pipelines

MLflow

Terraform

MLOps Platforms

Agentic AI Frameworks (CrewAI, AutoGen, LangGraph)

Knowledge Graphs

NLP & Machine Learning Fundamentals

2-5 years of experience in AI/ML, Generative AI, LLM Engineering, or MLOps.

Strong understanding of LLM lifecycle management and production deployment.

Experience building enterprise AI assistants, chatbots, search, or knowledge management solutions.

Excellent problem-solving and analytical skills.

Effective communication and collaboration abilities.