Senior AI/ML Engineer - MLOps & LLMOps Platforms

Senior AI/ML Engineer - MLOps & LLMOps PlatformsAbout the Role :

We are looking for a Senior AI/ML Engineer - MLOps & LLMOps Platforms to build and operationalize scalable AI/ML platforms and production-grade machine learning and Generative AI applications.

The role is focused on MLOps, LLMOps, model deployment, AI infrastructure, automation, monitoring, and production engineering.

You will work on building reliable platforms and workflows that enable teams to develop, deploy, monitor, and manage ML models and LLM-based applications at scale.

The ideal candidate combines strong Python and software engineering skills with hands-on experience in MLOps/LLMOps, Kubernetes, cloud platforms, CI/CD, model serving, MLflow, LLMs, RAG, and production AI systems.

Key Responsibilities :1. MLOps & LLMOps :

• Design and implement end-to-end MLOps and LLMOps pipelines for production AI/ML workloads.

• Build automated workflows for model training, validation, deployment, serving, monitoring, and lifecycle management.

• Develop CI/CD pipelines for ML models, LLM applications, and AI services.

• Implement model and application versioning, experiment tracking, testing, release management, and rollback strategies.

• Build scalable processes for deploying and managing ML models and LLM-based applications across cloud environments.

• Automate operational workflows to improve the reliability, scalability, and efficiency of AI systems.

2. AI/ML Platform Engineering :

• Build and maintain reusable platform components for AI/ML model deployment, inference, monitoring, and governance.

• Develop APIs, services, deployment frameworks, and tooling that enable engineering teams to operationalize AI solutions.

• Design infrastructure supporting real-time and batch model inference.

• Implement scalable model serving and inference architectures.

• Integrate AI/ML platforms with enterprise applications, data platforms, and cloud infrastructure.

3. GenAI & LLM Engineering :

• Productionize LLM and Generative AI applications using models from open-source and commercial ecosystems.

• Build and deploy RAG pipelines, embedding workflows, vector search, and semantic retrieval systems.

• Develop production-ready LLM applications with appropriate evaluation, monitoring, and deployment workflows.

• Work with LLM orchestration frameworks such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, or equivalent.