Agentic AI Engineer

Overview

Architects and builds the infrastructure and tooling that powers AI agent development across the Software Development Lifecycle (SDLC). Develops production-grade agentic systems, orchestration frameworks, and observability solutions that enable teams to build, deploy, and monitor reliable AI agents at scale. Plays a key role in defining and implementing the next generation of SDLC through AI-first innovation and comprehensive instrumentation.

What We're Looking For

You demonstrate sharp product sense for high-impact automation opportunities, technical taste in implementation decisions, and the ability to clearly articulate trade-offs. You know when to apply AI agent solutions versus simpler approaches and can explain the "why" behind architectural choices.

You excel at 0-to-1 (and 1-to-100) product development, comfortable operating in ambiguous environments where requirements emerge through experimentation and iteration rather than upfront specification.

Key Responsibilities

AI Agent Development & Automation:

• Develop production-grade AI agents that eliminate manual handoffs across the SDLC

• Create custom integrations and CLI tools that give agents deep understanding of internal systems and codebases

• Design comprehensive testing strategies to ensure agent reliability and output quality

• Implement "Golden Path" scaffolding that embeds organizational standards into new projects

• Build AI solutions that improve codebase navigation, documentation, and developer workflows

• Identify workflow bottlenecks and deliver measurable impact through intelligent automation

• Shape SDLC evolution by identifying AI-first opportunities and proving outcomes through experimentation

Agent Infrastructure & Platform:

• Architect and maintain production infrastructure supporting agent deployment, lifecycle management, and scaling

• Develop agent frameworks, templates, and SDKs that accelerate agent development

• Create governed Model Context Protocol (MCP) catalog enabling compliant agent-to-agent and agent-to-MCP communication

• Implement governance controls for agent behavior, permissions, and system access

Observability & Performance Analytics:

• Design and implement metrics, monitoring, and logging infrastructure for AI agents and development workflows

• Build dashboards that provide actionable insights into developer productivity, tool adoption, and agent performance

• Establish KPIs and measurement frameworks to quantify the impact of AI-powered automation

• Create alerting and anomaly detection systems to ensure reliability of agents and tooling

• Analyze telemetry data to identify optimization opportunities and guide strategic investment decisions

Collaboration & Impact:

• Partner across teams to drive adoption of AI-powered tooling and process transformation

• Stay current with LLM technologies and coach colleagues on AI-assisted development and automation best practices

• Rapidly prototype solutions to validate use cases and prove value quickly

• Communicate data-driven insights to stakeholders through clear visualizations and reports

Preferred Qualifications:

• 5-7+ years of software engineering experience building production systems

• Proven experience building agentic systems using LLM orchestration frameworks

• Hands-on expertise with AI-powered development tools (code assistants, AI-enhanced editors)

• Strong foundation in SDLC, system design, and internal tooling development

• Experience with observability tools and practices including metrics collection, logging frameworks, and dashboard development

• Full-stack technical proficiency:

• Languages: Java, Python, JavaScript/TypeScript

• Frameworks: Angular, Spring Boot

• CI/CD platforms and cloud infrastructure (AWS)

• Monitoring/observability tools (e.g., Prometheus, Grafana, CloudWatch)

• Passion for transforming software development through AI innovation and data-driven decision making

# LI-CGTS

# TS-2505