Qualifications

• 7+ years of experience in engineering/machine learning, including 3+ years as a Solution Architect.
• Deep proficiency in AWS (SageMaker), Azure (OpenAI), or GCP (Vertex AI).
• Expertise in generative AI architecture, focusing on RAG and LLM orchestration with tools like LangChain/LlamaIndex.
• Strong knowledge of scalable inference and serving frameworks such as vLLM, Triton, and TGI.
• Experience with AI data pipelines, feature stores, and vector databases like Pinecone and Milvus.
• Proficiency in MLOps & LLMOps tools including MLflow and LangSmith.
• Hands-on experience with containerization tools (Kubernetes, Docker) and Terraform.

Responsibilities

• Design and implement scalable AI solutions using AWS, Azure, or GCP.
• Architect and optimize generative AI systems, focusing on RAG, agentic workflows, and LLM orchestration.
• Oversee model serving and inference frameworks for high performance and reliability.
• Manage AI data pipelines and feature stores to support data-driven decisions.
• Implement MLOps and LLMOps practices for model tracking and monitoring.
• Utilize DevOps practices and Infrastructure as Code (IaC) for AI infrastructure deployment.
• Collaborate with cross-functional teams to deliver innovative AI solutions for B2B clients.

Benefits

• Flexible work environment in a supportive and innovative company culture.
• Opportunity to work with cutting-edge AI technologies and tools.
• Professional development and training in advanced cloud and AI strategies.
• Collaborative team atmosphere that encourages creativity and solution-driven thinking.