Senior AI Engineer

Macmillan•New York, NY

5d•$150,000 - $160,000•Remote

About The Position

This is a role for someone who has built and shipped AI-powered products at scale and now wants to shape the technical future of an industry that is being fundamentally transformed by AI. As a Senior AI Engineer (Full-Stack / Applications), you will design, build, and operate production-grade, cloud-native applications with AI at their core. This role sits at the intersection of strong software engineering and deep AI expertise, with a focus on turning state-of-the-art AI capabilities into robust, scalable, and high-quality user-facing products. This role prioritizes deep AI knowledge alongside strong software engineering skills to ensure an effective utilization of AI capabilities in production systems. You will work across the full lifecycle of AI-powered systems: from evaluating and optimizing models, to integrating them into end-to-end applications, to deploying, monitoring, and iterating on them in production. Candidates may bring deeper expertise in either application development or applied AI, and we value engineers who are excited to collaborate and grow across disciplines. We do not expect candidates to match every technology or qualification listed below. Strong candidates will bring deep expertise in some areas alongside curiosity, adaptability, and strong collaboration skills. In this role you will be flexibly assigned to short-term PoC projects, mid-term rapid MVP projects and long-term product development, based on your skills, interests, and company priorities. Our current and future tech-stack: Google Cloud, AWS, Firebase, Python, Typescript, Django, Terraform, Github, Cloud Firestore, EventArc, PubSub, GCIP, Cloud Storage, Cloud Functions v2, React, BigQuery, Github Actions, commercial AI services, Open Source AI Models, Langfuse, Snowflake, PostgreSQL.

Requirements

Pragmatic, result-oriented analytical problem solving skills that can overcome ambiguity.
Friendly, concise, audience-oriented communication in written and spoken form (English) with effective asynchronous communication.
Self-organized, purpose-driven individual that likes to collaborate with and enable their team.
A T-shaped engineer with strong general software engineering and AI skills, paired with deep expertise in at least one area: Frontend: Advanced experience with React and TS, building high-quality, user-facing apps. Backend: Deep experience designing and operating scalable, event-driven Python systems in cloud-native environments. AI: In-depth knowledge of frontier AI models, evaluation, integration into AI-powered product features, and production best practices.
Strong theoretical understanding and practical experience with current AI model internals.
Proven experience with AI model deployment & utilization strategies, evaluation methodologies, and monitoring frameworks in production environments.
Proven experience owning features end-to-end, from technical design to production.
Strong understanding of how to integrate AI into production systems, including the challenges of non-deterministic behavior.
Solid understanding and practical application of MLOps and LLMOps best practices for AI applications.
Familiarity and hands-on experience with major AI service providers and their offerings.
Working knowledge of fine-tuning methods and their appropriate application.
Strong software engineering skills with the ability to bridge AI research/concepts and robust, production-ready engineering implementations.
Experience breaking down complex AI objectives into requirements & actionable tasks.
Experience leveraging and refining agile practices and processes within an AI/ML team context, including effective requirements, design, and code reviews.
Proven experience in quickly assessing and adopting new technologies & frameworks.
Able to implement Clean Architecture & Onion Architecture principles.
Experience building and operating cloud-native systems, ideally on Google Cloud / Firebase with solid DevOps and MLOps fundamentals, including CI/CD, observability, and Infrastructure as Code.
Working knowledge of AI safety and security.
Solid testing discipline across the stack - unit, integration, and contract tests for both deterministic code and AI components.
Mentorship and technical-leadership experience: growing other engineers, leading design reviews, and influencing technical direction across teams.
Working knowledge of AI safety and security: OWASP Top 10 for LLMs, prompt injection, data exfiltration risks, content moderation, and PII / GDPR handling.
Solid testing discipline across the stack - unit, integration, and contract tests for both deterministic code and AI components.
Experience operating production systems on-call

Nice To Haves

Experience in the publishing or media industry
Experience with our entire TechStack
Experience with MLOps on Vertex AI & Weights and Biases
Experience in applied research
Experience applying & implementing SRE methodologies
Advanced understanding of ML, LLMs and trends in the AI market
Specialized, deep expertise in a relevant area.

Responsibilities

Own AI-powered features end-to-end, from scoping, design and requirements engineering to feature evaluation, deployment and operation.
Design, build and maintain cloud-native, event-driven AI applications across the full stack.
Evaluate, integrate, and optimize AI models for production use, balancing quality, latency, reliability and cost.
Collaborate effectively with cross-functional team members, including non-technical stakeholders, to refine requirements and align technical solutions with business objectives.
Build and maintain robust AI inference, evaluation, and monitoring pipelines.
Develop and implement robust, automated AI application evaluation frameworks.
Analyze AI application performance data and experiments, using insights for data-driven improvements.
Operate and improve production systems, taking ownership of reliability, quality, and technical debt.
Stay current with AI advancements and share knowledge to upskill the team.
Plan, track, and break down work effectively in an agile team.
Provide technical leadership, drive design reviews, write ADRs and contribute to hiring and onboarding.
Participate in on-call rotations, incident response and post-mortems for AI-powered systems and help define SLOs and error budgets.