We are seeking a highly skilled LLM Systems & MLOps Architect to drive end-to-end architecture, optimization, and deployment of large-scale AI and LLM-based systems. This role is ideal for experts who can blend deep machine learning understanding with strong distributed systems engineering and MLOps capabilities. You will lead the design and implementation of advanced ML production pipelines, optimize GPU-based training and serving infrastructures, and enable efficient delivery of large language model solutions across cloud environments.

Architect and build scalable ML pipelines supporting experiment tracking, model versioning, feature stores, and automated retraining workflows.

Develop high-performance APIs and microservices for real-time model inference and complex multi-model serving environments.

Implement best practices across ML lifecycle management using tools such as MLflow, SageMaker, Vertex AI, and Azure AI.

Design and optimize distributed GPU environments for training and inference of large language models.

Implement model and data parallelism strategies using frameworks such as DeepSpeed, vLLM, and other scalable serving runtimes.

Improve throughput, reduce latency, and optimize resource utilization for large-scale LLM deployments.

Lead fine-tuning and parameter-efficient training of LLMs and LVMs to enhance accuracy, adaptability, and latency.

Reduce compute cost and training cycle time through advanced optimization methods and architecture improvements.

Implement production-grade automation for training, deployment, monitoring, and rollback using Kubernetes, Docker, Helm, and orchestration systems.

Leverage modern AI workflow frameworks such as Langflow, Flowise, Langgraph, and LangChain for enterprise-scale LLM orchestration.

Build observability, metrics dashboards, and automated reliability systems using Prometheus, Grafana, FluentD, and ELK stack.

LLM Frameworks: Hugging Face transformers, Llama, Mixtral, Claude, GPT, Gemini

LLMOps & Tooling: MLflow, LangChain, LangGraph, LlamaIndex, Flowise, Bedrock, SageMaker, Vertex AI, Azure AI

Cloud: AWS, Azure, GCP

Databases & Warehousing: DynamoDB, Cosmos, MongoDB, RDS, PostgreSQL, Aurora, Spanner, BigQuery

DevOps & Infra: Kubernetes, Docker, Prometheus, Grafana, FluentD, ELK Stack

Languages: Python, SQL, JavaScript

Bonus Certifications: AWS Pro Solutions Architect, AWS ML Specialty, Azure Solutions Architect Expert

Problem-solver passionate about scaling AI systems and pushing boundaries of LLM performance.

Strong communicator who thrives in cross-functional collaboration.

Curious and research-driven, staying current with emerging AI and distributed computing trends.