Sr AI Engineer

• AI Product Delivery: Lead and actively contribute to the development of AI products, pilots, and enterprise solutions with a focus on writing clean, maintainable code. • Architecture & Design: Design and architect scalable Generative AI solutions, including LLM pipelines, Agentic workflows, Model Context Protocol (MCP), Graph/RAG architectures, and complex prompt-based applications. • Cloud Infrastructure: Implement highly scalable, cloud-native solutions leveraging modern AWS services. • Optimization & Innovation: Optimize the performance of production AI products. Drive continuous learning and experimentation with cutting‑edge Gen AI methods, frameworks, APIs, and emerging toolchains. • Cross-Functional Collaboration: Partner closely with Product Managers, Data Scientists, and domain experts to translate business requirements into robust technical solutions. • Technical Leadership: Act as an organization-wide Subject Matter Expert (SME) on Gen AI technologies to help shape the company's long‑term AI strategy and technical roadmap. • Project Ownership: Manage timelines, deliverables, and project milestones using Agile practices (Scrum/Kanban) to ensure reliable end‑to‑end product delivery. • Operations & Governance: Monitor operational metrics and incident data to improve system reliability. Ensure strict adherence to corporate governance and DevSecOps protocols. Required Experience & SkillsTechnical Expertise • 10 + years of progressive engineering experience. - DO NOT SUBMIT LESS THEN 10 YRS EXP. • 1–2 years of direct experience leading emerging technology or enterprise AI initiatives. • Strong familiarity with the full Software Development Life Cycle (SDLC) best practices and DevSecOps protocols. • Generative AI & LLMs: Core proficiency with commercial and open-source models (GPT, Claude, Gemini, LLaMA), advanced prompt engineering, and LLM fine-tuning techniques. • AI Frameworks & Architectures: Deep hands‑on experience with LangChain, LlamaIndex, Amazon Bedrock, Agentic AI, MCP, and Graph/RAG architectures. • Databases & Orchestration: Proven experience with Vector and Graph databases (Weaviate, Milvus, PGVector, Neo4J, Neptune), query optimization, and ETL orchestration using Apache Airflow/DAGs. • Cloud & DevOps (AWS): Comprehensive knowledge of AWS infrastructure, including computation (EKS, Fargate, Lambda, EC2), networking/load balancing (ELB, GLB, NLB), data analytics (Athena, Glue, Lake Formation), and Infrastructure as Code (Terraform, Puppet, Docker). • Testing & Analytics: Familiarity with automated AI and web testing frameworks (Ragas, Playwright, Zephyr, Selenium) and Business Intelligence tools (QuickSight, Tableau). #J-18808-Ljbffr