As a Machine Learning Systems Architect, your primary responsibility will be to lead the architecture, development, and deployment of scalable machine learning systems with a focus on real-time inference for Large Language Models (LLMs) to serve multiple concurrent users. To achieve this, you will: - Optimize inference pipelines using high-performance frameworks such as vLLM, Groq, ONNX Runtime, Triton Inference Server, and TensorRT to minimize latency and cost. - Design and implement agentic AI systems using frameworks like LangChain, AutoGPT, and ReAct for autonomous task orchestration. - Fine-tune, integrate, and deploy foundation models like GPT, LLaMA, Claude, Mistral, Falcon, and others into intelligent applications. - Develop and maintain robust MLOps workflows to manage the entire model lifecycle including training, deployment, monitoring, and versioning. - Collaborate closely with DevOps teams to implement scalable serving infrastructure by leveraging containerization (Docker), orchestration (Kubernetes), and cloud platforms (AWS, GCP, Azure). - Implement retrieval-augmented generation (RAG) pipelines that integrate vector databases such as FAISS, Pinecone, or Weaviate. - Build observability systems for LLMs to monitor prompt performance, latency, and user feedback. - Work collaboratively with research, product, and operations teams to deliver production-grade AI systems that can handle real-world traffic patterns effectively. - Stay informed about emerging AI trends, hardware acceleration techniques, and actively contribute to open-source or research initiatives whenever possible. Additionally, it is important to note that this job requires you to have a strong background in machine learning and experience with various frameworks and tools mentioned in the job description.,

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