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Lead Agentic AI Architect

Xactware
Jersey City, USA
Full-time
Lead · 10+ years experience
Hybrid
Discovered 3 weeks ago
awsbedrockpython

Full Job Posting

Qualifications

  • Engineering leadership. 10+ years in software engineering, including 4+ years leading AI and agentic architecture or technical delivery across multiple teams, or equivalent demonstrated expertise.
  • Production AI delivery. Has deployed and operated multiple LLM or agentic application in production; strong Python, APIs, distributed systems, RAG and agent orchestration skills.
  • Cloud and collaboration. Experience with cloud deployment, containers, CI/CD, evaluation and observability; able to explain trade-offs and influence technical and business stakeholders.
  • AWS and Amazon Bedrock, including AgentCore; LangGraph, Stands or comparable agent frameworks; MCP; semantic layers, ontologies or knowledge graphs; insurance or other regulated data products; AI-enabled SDLC practices.
  • #LI-MB1
  • #LI-Hybrid

Responsibilities

  • Set portfolio architecture. Define reference architectures, reusable agent services and engineering standards. Select models, frameworks and build-versus-buy options based on quality, cost and business needs.
  • Lead through implementation. Write production code, review designs and pull requests, resolve complex technical issues and coach teams through delivery and adoption.
  • Design dependable workflows. Implement tool calling, orchestration, context and memory, retries, execution limits and human approvals. Choose simple automation, a single agent or multiple agents as the problem warrants.
  • Connect enterprise knowledge. Build Agentic, retrieval-augmented generation (RAG), structured data access and API or Model Context Protocol (MCP) integrations with permission-aware retrieval and source traceability.
  • Establish AI evaluation. Partner with QA and domain experts on test datasets, task-success measures, groundedness, tool-call accuracy, adversarial testing and release acceptance criteria.
  • Own production readiness. Implement CI/CD, versioning, monitoring, agent tracing, fallbacks and rollback. Track reliability, latency and cost per successful task, and support incident resolution.
  • Embed security and governance. Apply least-privilege tool access, sensitive-data protection, prompt-injection defenses, auditability and approval controls with Security and governance teams.
  • Advance AI-enabled development. Introduce and govern AI-assisted engineering practices such as coding agents, AI code review and test generation, and measure their effect on delivery speed and quality.
  • Drive portfolio outcomes. Prioritize use cases with Product, maintain the technical roadmap and share reusable components and lessons across teams to improve delivery speed and customer value.

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