AI Engineer / AI Architect

Responsibilities

• AI Architecture & Technical Strategy
• Define AI system architecture aligned to business objectives and long-term scalability
• Translate ambiguous AI opportunities into structured technical approaches
• Design modular, extensible AI platforms supporting experimentation and production workloads
• Establish architecture decision records (ADRs) and technical documentation standards
• AI System Design & Implementation
• Lead design and implementation of ML systems, model services, and inference pipelines
• Build scalable training, evaluation, and deployment workflows
• Architect data ingestion, feature engineering, and model serving strategies
• Ensure reliability, observability, and performance optimization of AI systems
• AI Platform & Infrastructure
• Design and implement end-to-end AI pipelines and agentic orchestration frameworks.
• Establish LLMOps best practices including CI/CD, prompt versioning, and real-time model observability.
• Optimize distributed inference systems and retrieval-augmented generation (RAG) performance.
• Ensure AI infrastructure aligns with security, compliance, and GPU/token cost-efficiency standards.
• Research-to-Production Acceleration
• Bridge research and engineering by operationalizing prototypes into production-ready systems
• Define evaluation metrics, validation processes, and rollout strategies
• Improve iteration velocity through tooling and automation
• Technical Leadership & Mentorship
• Mentor junior and mid-level AI engineers
• Lead code reviews and enforce engineering standards
• Guide teams in making informed tradeoffs across performance, scalability, cost, and complexity
• Contribute to shared AI best practices and internal accelerators
• Cross-Functional Collaboration
• Partner with product, engineering, and executive stakeholders to shape AI roadmaps
• Communicate technical strategy and system tradeoffs clearly to non-technical audiences
• Align AI initiatives with business value and measurable outcomes
• Required Skills & Qualifications
• 5+ years of experience in AI/ML engineering or applied machine learning
• Proven experience designing and deploying production AI systems
• Strong software engineering background (Python or similar)
• Experience with distributed systems and scalable data architectures
• Deep understanding of MLOps, model lifecycle management, and AI infrastructure
• Experience building and maintaining ML pipelines and automated deployment workflows
• Strong problem-solving skills and architectural judgment
• Demonstrated leadership and mentoring experience
• Excellent communication skills for technical and executive stakeholders

Requirements

• Experience with generative AI, LLM systems, or retrieval-augmented architectures
• Exposure to multi-agent or agentic AI systems
• Experience with cloud-based AI platforms and infrastructure-as-code
• Background in consulting or professional services environments
• Experience designing AI systems in regulated or security-sensitive domains
• Hands-on AWS experience supporting cloud-native or AI/ML production systems.
• AWS certifications (Associate or Professional level) or equivalent practical AWS expertise.
• Personal Competencies
• Architectural Thinking - Designs systems that scale and evolve
• Technical Ownership - Drives AI initiatives from concept to production
• Engineering Rigor - Prioritizes reliability, observability, and maintainability
• Mentorship - Elevates team capability through guidance and example
• Execution Focus - Delivers production-ready systems, not just prototypes
• Comfort in Ambiguity - Translates unclear AI challenges into structured solutions
• Why Join Robots & Pencils?
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Additional Information

Robots & Pencils is seeking an AI Engineer / AI Architect to design, build, and scale production-grade AI systems that deliver measurable business value. This is a hands-on technical leadership role. You will define system architectures, lead implementation of scalable AI platforms, and guide teams through the full lifecycle of AI development, from experimentation and model iteration to secure, observable production deployment. As a Level 4 leader, you are expected to own complex AI systems end-to-end, influence technical direction, mentor engineers, and make principled architectural tradeoffs under real-world constraints.

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