Architect

OverviewIn this role you will design and scale production-grade AI solutions across cloud platforms, focusing on generative AI architectures and AI-driven workflows. You will collaborate with cross-functional teams to deliver B2B AI capabilities, balancing performance, reliability, and data-driven decision making. You will lead MLOps/LLMOps practices and infrastructure automation to support rapid, secure deployment at scale. This position offers the chance to shape AI systems that power real-world business outcomes and stay at the forefront of cloud AI innovation.

ResponsibilitiesDesign and implement scalable AI solutions on cloud platforms (AWS, Azure, or GCP).Architect and refine generative AI systems with RAG, agentic workflows, and LLM orchestration.Oversee model serving and inference frameworks for high performance and reliability.Manage AI data pipelines, feature stores, and vector databases to enable data-driven decisions.Implement MLOps and LLMOps practices for model tracking, versioning, evaluation, and monitoring.Apply DevOps practices and Infrastructure as Code to AI infrastructure deployment.Collaborate with cross-functional teams to deliver AI solutions for B2B clients.Key requirements7+ years in engineering/ML with at least 3 years as a Solution Architect.Deep proficiency in AWS (SageMaker), Azure (OpenAI), or GCP (Vertex AI).Expertise in generative AI architecture, including RAG and LLM orchestration (LangChain/LlamaIndex).Strong knowledge of scalable inference and serving frameworks (vLLM, Triton, TGI).Experience with AI data pipelines, feature stores, and vector databases (Pinecone, Milvus).Proficiency in MLOps & LLMOps tools (MLflow, LangSmith).Hands-on experience with containerization (Kubernetes, Docker) and Terraform.Proven experience as a Solution Architect in a B2B environment.strong communicationleadershipability to work in a fast-paced, collaborative environmentAWS SageMakerAzure OpenAIGCP Vertex AI