Principal, AI Forward Deployment Engineer

Responsibilities

• AI Application Development & Deployment: Architect, build, and deploy enterprise-grade AI-powered applications using modern backend technologies (Python, Node.js, FastAPI, Express).
• Design and implement robust APIs and microservices architectures that integrate AI/ML models—including LLMs and agentic systems—with business systems at scale.
• Lead containerization strategies using Docker and manage complex deployments via Kubernetes (EKS/ECS) with a focus on reliability, scalability, and performance.
• Design and implement event-driven architectures using Kafka or similar streaming platforms for real-time data processing and AI inference.
• Take full ownership of end-to-end delivery from technical scoping and architecture design through production deployment, monitoring, optimization, and ongoing operational excellence.
• Rapid Prototyping & Problem Discovery: Deconstruct ambiguous, complex business problems into actionable AI solutions by deeply understanding operational context, system constraints, and stakeholder priorities.
• Rapidly build proof-of-concept applications using appropriate technology stacks to validate approaches and demonstrate business value.
• Architect scalable, production-ready solutions that account for performance, reliability, security, and maintainability from inception.
• Lead iterative development cycles based on user feedback, refining solutions until they deliver measurable, quantifiable business impact.
• Serve as a trusted advisor to business units on what is technically feasible and strategically valuable.
• Stakeholder Engagement & Technical Translation: Serve as the senior technical point of contact and trusted advisor for business stakeholders during AI deployments.
• Communicate complex technical concepts—including architecture decisions, trade-offs, risks, and recommendations—to executive leadership and non-technical audiences with clarity and confidence.
• Lead cross-functional collaboration with data scientists, data engineers, platform teams, infrastructure teams, microservice teams, security, privacy, and product managers to ensure solutions meet rigorous technical standards and business objectives.
• Build and maintain strong relationships with business partners through consistent delivery, transparent communication, and a demonstrated commitment to their success.
• Field Insights & Platform Feedback: Champion continuous improvement by bringing strategic learnings from field deployments back to the core AI/Data and Platform teams.
• Identify opportunities to improve tools, infrastructure, and reusable components that benefit the broader organization.
• Author and maintain comprehensive documentation including solution architectures, design patterns, and operational runbooks that enable knowledge transfer and accelerate future deployments.
• Proactively identify gaps in platform capabilities (CI/CD, observability, infrastructure, developer experience) and advocate for improvements with supporting business justification.
• Define and elevate engineering standards and best practices across the AI organization, mentor junior and mid-level engineers on these standards.

Requirements

• Bachelor's degree in Computer Science, Software Engineering, Data Science or related field
• Master's degree preferred but not required
• Certifications (required at least 1): AWS certifications (Solutions Architect, Developer Associate, Machine Learning Specialty), Kubernetes certifications (CKA, CKAD)
• Relevant AI/ML or cloud certifications
• Minimum 6-8+ years of software engineering experience with a focus on backend/full-stack development
• 4+ years deploying AI/ML solutions in production environments
• Led/architected 3+ production AI systems
• Strong hands-on experience with Docker, Kubernetes, and cloud-native architectures
• Experience building and operating event-driven or streaming data systems (Kafka, Kinesis)
• Track record of delivering technical solutions in ambiguous, fast-moving environments
• Track record leading technical engagements with senior/executive stakeholders
• Mentored junior engineers; led cross-functional delivery teams