We respectfully request that 3rd parties refrain from contacting us regarding this posting.
Sr. AI Architect
Overview
LABUR is partnering with a client to identify a Sr. AI Architect who will lead the design, development, and delivery of an enterprise-scale agentic AI platform built on AWS. This hands-on architect will own both the blueprint and the build, shaping LLM-powered assistants, RAG pipelines, and agent-based systems while translating complex business problems into scalable technical solutions. Working closely with business stakeholders, this individual will identify high-value use cases, drive platform adoption, and establish governance and best practices across the AI portfolio, balancing speed, quality, and scalability at every stage.
Responsibilities
Lead the design and hands-on implementation of an enterprise agentic AI platform leveraging AWS Bedrock, AgentCore, and foundation models including Claude, Amazon Titan, and OpenAI models available through Bedrock
Architect and build RAG pipelines end to end, covering ingestion, embeddings, vector indexing, and semantic search optimization for performance and relevance
Develop LLM-powered assistants and copilots that execute real-world workflows such as customer support automation, knowledge summarization, and enterprise search using agentic AI frameworks like LangChain Agents and Bedrock Agents
Engage directly with business stakeholders to understand requirements, identify high-value use cases, and translate them into well-scoped technical solutions built on the agentic platform
Define and enforce reference architectures, reusable components, and best-practice artifacts to ensure consistency, maintainability, and scalability across AI initiatives
Establish and drive LLMOps practices covering prompt and model versioning, agent lifecycle management, feedback loops, and continuous evaluation
Ensure AI solutions adhere to regulatory and validation standards (e.g., FDA validation for AI software), internal governance, security protocols, and responsible AI principles
Mentor engineering teams on generative AI techniques, fine-tuning, prompt engineering, ethics, and explainability while rapidly prototyping new agentic and multimodal workflows to validate feasibility
Qualifications
10+ years of professional experience including 7+ years in software engineering and AI/ML, with 3+ years specializing in Generative AI and LLMs
Proven track record designing, deploying, and scaling enterprise AI/LLM solutions with a hands-on approach to both architecture and implementation
Strong expertise with AWS services (Bedrock, AgentCore, Lambda, S3, ECS, SageMaker) and direct experience building agentic platforms on AWS infrastructure
Deep knowledge of GenAI toolchains including LangChain, LlamaIndex, and multi-agent orchestration frameworks
Extensive experience with vector search, embeddings, retrieval pipelines, and RAG architectures including chunking, indexing, and orchestration strategies
Proficiency in MLOps/LLMOps practices including CI/CD, model and version management, observability, and monitoring production AI systems (e.g., Datadog, MLflow)
Solid understanding of enterprise security, compliance, data governance, AI ethics, explainability, and bias mitigation for AI systems
Proficiency in Python, FastAPI, and WebSockets with excellent communication skills and demonstrated ability to distill complex business requirements into scalable AI solutions