Senior Agentic AI Engineer

We are seeking a Senior Agentic AI Engineer to design, build, and optimize production-grade agentic AI solutions using large language models, advanced reasoning frameworks, and cloud-native architectures. This role will focus on building intelligent, scalable, and observable AI systems on AWS, with strong emphasis on AWS Bedrock AgentCore, multi-agent orchestration, A2A/MCP integration, RAG, NL2SQL, and end-to-end deployment of enterprise AI capabilities. This aligns with the core responsibilities and technologies listed in your draft, including Bedrock AgentCore components, A2A/MCP servers, Strands Agents, observability, LLM engineering, vector retrieval, and agent frameworks. Key Responsibilities Design, build, and optimize agentic AI applications and product features using AWS Bedrock AgentCore Develop serverless, scalable, and production-ready AI architectures on AWS Implement and operate A2A and MCP servers on AWS, and integrate them with Bedrock Agents and Converse APIs Orchestrate multi-agent workflows and reasoning pipelines using frameworks such as Strands Agents Build robust observability and auditability into agent systems using CloudWatch metrics, traces, and logs Develop and improve NL2SQL / Text-to-SQL pipelines using LLMs and AI/ML techniques Design and optimize RAG pipelines using vector databases, embeddings, and structured/unstructured enterprise data Integrate AI systems with data connectors, APIs, and gateway services to enable seamless enterprise workflows Partner with product managers, data engineers, UX teams, and stakeholders to deliver measurable business impact Contribute to the AI roadmap, solution design, evaluation standards, and production deployment best practices Required Skills & Experience 7+ years of software engineering experience, with strong hands-on work in Python and distributed/cloud-based application development 3+ years of experience in Generative AI / LLM application development Strong hands-on experience with AWS Bedrock AgentCore, especially Memory, Gateway, Runtime, Identity, Observability, or related services Experience building agentic workflows, multi-agent systems, or reasoning-based AI applications Strong understanding of LLMs, including prompt engineering, evaluation, fine-tuning concepts, and production usage patterns Hands-on experience with RAG architectures, vector databases, embedding models, and enterprise retrieval workflows Experience building NL2SQL / Text-to-SQL solutions using AI/ML or LLM-based approaches Hands-on experience with one or more agent frameworks such as Strands, LangGraph, LangChain Agents, Semantic Kernel, or CrewAI Experience integrating AI services with APIs, data connectors, and gateway-based enterprise systems Strong problem-solving skills with the ability to work in a fast-paced, innovation-driven environment Strong communication and stakeholder management skills, with the ability to explain complex AI concepts clearly Preferred Qualifications Experience with AWS-native observability and monitoring for AI applications Exposure to enterprise AI ecosystems such as OpenAI, Anthropic, Azure AI Foundry, Copilot Studio, Google Gemini, or Microsoft 365 Copilot Experience working with structured and unstructured data for AI training, retrieval, and reasoning workflows Experience in productionizing AI solutions with governance, auditability, security, and measurable business outcomes Familiarity with enterprise-scale solution design and cross-functional delivery Nice-to-Have Skills Experience with fine-tuning workflows or LLM adaptation strategies Exposure to agent governance, evaluation frameworks, and AI safety/guardrails Experience supporting data-driven transformation initiatives across business teams Prior experience in highly collaborative, research-driven, or innovation-led engineering environments What Success Looks Like Build and deploy scalable agentic AI systems that are reliable, observable, and enterprise-ready Deliver measurable improvements in automation, reasoning, retrieval quality, and user productivity Establish strong engineering patterns for multi-agent architecture, RAG, and Bedrock-based AI delivery Serve as a key technical contributor in shaping the organization’s agentic AI strategy My take on this rewrite This version fixes the biggest problems in your draft: gives the role a stronger title than “Agentic Developer” separates responsibilities, required skills, and preferred skills removes awkward fragment-style lines from the original draft such as the incomplete responsibility phrasing keeps the real technical stack from your document: Bedrock AgentCore, A2A/MCP, Strands, CloudWatch, LLMs, Python, NL2SQL, vector databases, RAG, and agent frameworks Pay: $70.00 - $80.00 per hour Work Location: In person