Senior AI Engineer

Our client, a top-tier Management Consulting firm, is seeking a highly skilled Senior AI Engineer to support advanced AI initiatives for a leading US Bank. This role will focus on designing and deploying enterprise-grade Generative AI applications leveraging foundation models, agentic frameworks, and modern cloud-native architectures. The ideal candidate will bring deep hands-on expertise in GenAI (RAG, GraphRAG, knowledge graphs, ontology extraction) along with experience working in fast-paced, start-up or early-stage environments where speed, ownership, and innovation are critical. Responsibilities & Requirements Design and build advanced GenAI applications leveraging foundation models and modern architectures including RAG, GraphRAG, and agentic workflows Develop and deploy autonomous AI agents using modern agentic frameworks Implement solutions involving knowledge graphs, entity extraction, ontology modeling, and embeddings at scale Build scalable Python-based full-stack applications and REST APIs supporting AI-driven use cases Integrate AI/LLM systems using Model Context Protocol (MCP) and related integration patterns Deploy and manage AI workloads across cloud platforms (AWS, Azure, or GCP) Apply LLMOps/MLOps best practices including CI/CD, model monitoring, observability, and performance optimization Collaborate with cross-functional teams across engineering, data, and business stakeholders Provide technical leadership and contribute to innovation in enterprise AI solutions Required Experience & Skills 5+ years of AI/ML-focused software engineering experience Strong hands-on experience with GenAI architectures including RAG, GraphRAG, knowledge graphs, entity/ontology extraction, and embeddings Proven production experience building and deploying LLM-based and agentic systems Expert-level Python development (backend focused; no Java required) Strong experience designing scalable APIs and full-stack AI applications Experience working in start-up or early-stage environments with fast-paced delivery expectations and high ownership Experience deploying AI solutions in cloud environments (AWS, Azure, or GCP) Familiarity with LLMOps/MLOps tools and best practices Strong problem-solving, collaboration, and communication skills

Created: 2026-03-04