Engineering Lead - AI

• Extensive, hands-on software engineering experience, with a proven track record of building and operating complex systems in production environments.
• Strong executive communication and stakeholder management skills, with the ability to translate complex technical concepts into clear, business-relevant outcomes.
• Ability to independently design and code across multiple technical components, while guiding and elevating the work of senior engineers.
• Deep technical expertise across the full stack, including cloud-native architectures, distributed systems, and data-intensive platforms.
• Strong command of non-functional requirements, including reliability, availability, scalability, performance, and cost optimization, and the ability to make sound technology and architecture decisions as systems evolve over time.
• Demonstrated experience delivering production-grade Generative AI and Agentic AI solutions, including:
• LLM-powered applications and services
• Agentic workflows and orchestration frameworks
• Model integration, evaluation, and lifecycle management
• MLOps / LLMOps pipelines and operational practices
• Proven experience partnering with Data Science and AI Research teams to operationalize models and AI capabilities at enterprise scale.
• Ability to drive the design and delivery of AI-first architectures, including LLM-powered services, agentic workflows, orchestration layers, and human-in-the-loop systems.
• Experience building robust data and software foundations that enable advanced analytics, real-time AI inference, and intelligent decisioning at scale.

Essential Functions of the Job:

• Engineering Ownership & Delivery Accountability
  • Own end-to-end technical delivery of AI and GenAI solutions—from design through production and BAU support.
  • Act as the technical authority for the build team, accountable for:
    • Code quality and engineering standards
    • Security, privacy, and compliance
    • Reliability, scalability, performance, and cost
    • Operational readiness and supportability
  • Make hard engineering trade-offs balancing latency, accuracy, cost, reliability, and scale.
  • Own production systems post go-live, including incident analysis, performance tuning, and architectural evolution.

• Hands-on GenAI & AI Systems Engineering
• • Collaborate with solution architecture on design and own build production-grade GenAI systems, including:
    • Retrieval-Augmented Generation (RAG) pipelines
    • Agentic workflows and tool-based orchestration
    • Prompt pipelines, routing, and integration layers
    • Human-in-the-loop and safety guardrails
  • Work shoulder-to-shoulder with AI Engineers and Data Scientists to:
    • Productionize models and LLM pipelines
    • Implement evaluation, monitoring, and observability
    • Optimize inference cost, performance, and reliability
  • Ensure experimental AI capabilities are engineered into real systems, not isolated prototypes.

• Architecture & Engineering Standards
  • Define and enforce reference architectures and engineering standards for AI and GenAI systems.
  • Drive consistent adoption of:
    • Clean and modular architecture patterns
    • Reusable components and shared frameworks
    • CI/CD, DevOps, and cloud-native practices
  • Partner with architecture, platform, security, and data teams while retaining final accountability for build quality.

• AI Platform Engineering & MLOps / LLMOps
  • Build and evolve AI platforms that support:
    • Model lifecycle management
    • LLMOps / MLOps pipelines
    • Evaluation, monitoring, and drift detection
    • Secure access, auditability, and governance
  • Ensure AI systems meet enterprise non-functional requirements over time, not just at launch.

• Team Leadership & Capability Building
  • Lead and grow a team of senior engineers within the AI & Automation build function.
  • Coach engineers on:
    • Backend and