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Senior AI Engineer
Location
New York
Posted
6 days ago
Salary
$150K - $160K / year
Seniority
Senior
Job Description
Senior AI Engineer
Macmillan
• 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.
Job 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.
- 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.
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