Jet Support Services, Inc. (JSSI)

AI Engineer

• Design, ship, and operate production-grade AI systems from prototype to production
• Report to the Director of Engineering and partner with the AI Solution Architect, AI Verification Architect, and AI First Product team
• Turn product specifications and Agile artifacts into AI agents, LLM-powered features, and automation
• Integrate AI systems with JSSI enterprise systems and own their reliability, accuracy, and business impact in production
• Drive refinement of the AI agentic pipeline and iterative development loop
• Direct the design, build, and delivery of production-grade AI features and agents across JSSI's SaaS platforms
• Implement and integrate AI tools, agents, agent skills, and services across specification, development, review, testing, documentation, and release
• Build multi-agent workflows for orchestration, reasoning, planning, autonomous task execution, queues, caching, and scalable APIs
• Translate Agile artifacts into agent-ready specifications and build and consume MCP servers exposing JSSI enterprise systems as model-ready tools
• Use Claude Code to develop, review, test, and document software in tight iterative cycles, with a goal of 100% AI-generated code
• Steward the context store, coding standards, patterns, domain knowledge, and project memory
• Derive tests from acceptance criteria and make specifications executable for verification
• Build, operate, and maintain agent-based automation coordinating LLMs, tools, APIs, and enterprise data including Fabric, Salesforce, D365 F&O, and JSSI proprietary platforms
• Own agent reliability, observability, fallback behavior, and lifecycle management in production
• Direct coding agents to build internal and external applications and services such as dashboards and microservices
• Create and refine AI prompts, agent skills, and context packages; monitor, troubleshoot, and optimize automation
• Partner with stakeholders to identify and prioritize high-impact automation opportunities
• Collaborate with architects, engineering peers, product teams, citizen developers, and business stakeholders
• Follow responsible AI practices including fairness, explainability, model monitoring, ethics, and regulatory alignment

• Define and execute delivery roadmaps for applications using Claude Code Enterprise and agentic development
• Embed AI across the delivery cycle, including spec-driven intent, agent execution, automated verification, and production insight
• Maintain accountability for the reliability, accuracy, and business impact of shipped software, AI agents, LLM-powered features, and automation
• Align engineering standards, roadmaps, reusable patterns, and architecture recommendations with peer leaders
• Prototype, review examples, challenge assumptions, and apply hands-on engineering judgment
• Manage hiring, onboarding, performance management, and career development for a team of three to eight AI solution architects, engineers, and verification architects
• Operate a unified delivery model across onshore and offshore teams
• Coach solution architects and build team capability through learning paths, enablement sessions, office hours, and communities of practice
• Drive responsible enterprise adoption of Claude Code through onboarding, usage patterns, guardrails, training, and coaching
• Create reusable engineering assets, prompt libraries, MCP and context-sharing patterns, testing patterns, API standards, architecture decision records, and secure coding standards
• Establish AI First governance covering code ownership, reviews, IP and secrets protection, auditability, model usage, and human accountability
• Ensure responsible AI practices covering fairness, explainability, monitoring, security, and regulatory alignment
• Integrate AI systems and automation with Salesforce, Dynamics 365 F&O, Microsoft Fabric, and proprietary products
• Standardize MCP server development and consumption for enterprise systems
• Establish observability and lifecycle-management standards for production automations
• Define and report engineering, AI adoption, productivity, quality, reliability, and cost metrics
• Build relationships across Engineering, Product, Architecture, Security, Infrastructure, Data, Finance, Sales, and Operations
• Evaluate AI engineering capabilities and vendors for JSSI’s Azure/Microsoft environment and enterprise risk posture
• Communicate progress, risks, investment needs, adoption, and business impact to executives
• Partner with Product and business stakeholders to deliver on time, on target, and on budget

AI Engineer

Director of AI Engineering