Data Scientist-Data Science-Gen AI Engineer

As a Machine Learning Systems Architect, your role will involve leading the architecture, development, and deployment of scalable machine learning systems with a focus on real-time inference for LLMs serving multiple concurrent users. You will be responsible for optimizing inference pipelines using high-performance frameworks such as vLLM, Groq, ONNX Runtime, Triton Inference Server, and TensorRT to minimize latency and cost. Your key responsibilities will include: - Designing and implementing agentic AI systems utilizing frameworks like LangChain, AutoGPT, and ReAct for autonomous task orchestration. - Fine-tuning, integrating, and deploying foundation models including GPT, LLaMA, Claude, Mistral, Falcon, and others into intelligent applications. - Developing and maintaining robust MLOps workflows to manage the full model lifecycle including training, deployment, monitoring, and versioning. - Collaborating with DevOps teams to implement scalable serving infrastructure leveraging containerization (Docker), orchestration (Kubernetes), and cloud platforms (AWS, GCP, Azure). - Implementing retrieval-augmented generation (RAG) pipelines integrating vector databases like FAISS, Pinecone, or Weaviate. - Building observability systems for LLMs to track prompt performance, latency, and user feedback. - Working cross-functionally with research, product, and operations teams to deliver production-grade AI systems handling real-world traffic patterns. - Staying updated on emerging AI trends, hardware acceleration techniques, and contributing to open-source or research initiatives where possible. Your qualifications should include expertise in machine learning systems architecture, proficiency in high-performance frameworks, experience with agentic AI systems, and a strong background in MLOps workflows. Additionally, you should have a proven track record of collaboration with DevOps teams, experience in implementing scalable serving infrastructure, and familiarity with retrieval-augmented generation pipelines. Your ability to work cross-functionally and stay updated on emerging AI trends will be key to success in this role. As a Machine Learning Systems Architect, your role will involve leading the architecture, development, and deployment of scalable machine learning systems with a focus on real-time inference for LLMs serving multiple concurrent users. You will be responsible for optimizing inference pipelines using high-performance frameworks such as vLLM, Groq, ONNX Runtime, Triton Inference Server, and TensorRT to minimize latency and cost. Your key responsibilities will include: - Designing and implementing agentic AI systems utilizing frameworks like LangChain, AutoGPT, and ReAct for autonomous task orchestration. - Fine-tuning, integrating, and deploying foundation models including GPT, LLaMA, Claude, Mistral, Falcon, and others into intelligent applications. - Developing and maintaining robust MLOps workflows to manage the full model lifecycle including training, deployment, monitoring, and versioning. - Collaborating with DevOps teams to implement scalable serving infrastructure leveraging containerization (Docker), orchestration (Kubernetes), and cloud platforms (AWS, GCP, Azure). - Implementing retrieval-augmented generation (RAG) pipelines integrating vector databases like FAISS, Pinecone, or Weaviate. - Building observability systems for LLMs to track prompt performance, latency, and user feedback. - Working cross-functionally with research, product, and operations teams to deliver production-grade AI systems handling real-world traffic patterns. - Staying updated on emerging AI trends, hardware acceleration techniques, and contributing to open-source or research initiatives where possible. Your qualifications should include expertise in machine learning systems architecture, proficiency in high-performance frameworks, experience with agentic AI systems, and a strong background in MLOps workflows. Additionally, you should have a proven track record of collaboration with DevOps teams, experience in implementing scalable serving infrastructure, and familiarity with retrieval-augmented generation pipelines. Your ability to work cross-functionally and stay updated on emerging AI trends will be key to success in this role.