Senior AI Engineer

Anaplan

Senior AI Engineer developing and deploying Generative AI and Machine Learning systems. Owning architecture and collaborating with teams to build innovative capabilities at Anaplan.

Posted 6/6/2026full-timeRemote • Pennsylvania • 🇺🇸 United StatesSeniorWebsite

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Hard Skills

Generative AIMachine Learningbackend API servicesmodel integrationmodel monitoringprompt engineeringRAG implementationMLOpsLLMOpsPython

Soft Skills

ownershipmentoringtechnical design discussionscode reviews

Tools & Technologies

Anaplanobservability toolsmonitoring toolslogging tools

Industry Keywords

model lifecycle developmentconversational AIagentic frameworksautonomous agent architecturesmodel inference pipelines

Tech Stack

Tools & technologies

Python

About the role

Key responsibilities & impact

Contribute to the architecture and take ownership of the design, development, and deployment of scalable Generative AI and Machine Learning systems into production environments.
Develop end-to-end GenAI features, including backend API services, model integration, model monitoring, evaluations, and deployments.
Integrate and optimize LLMs for specific business planning use cases, including prompt engineering and RAG implementation.
Build cutting-edge conversational interfaces and agentic workflows that make complex planning tasks accessible through natural language.
Implement evaluation frameworks to measure and improve GenAI feature quality, including accuracy, latency, and user satisfaction metrics.
Design and develop APIs that expose AI capabilities to Anaplan's platform and third-party integrations.
Optimize model inference pipelines for performance, cost, and scalability in production environments.
Implement monitoring, logging, and observability for GenAI systems to track usage, errors, and model behavior.
Participate in code reviews, lead technical design discussions, and mentor junior and mid-level engineers.

Requirements

What you’ll need

Extensive hands-on experience in AI, ML, or related engineering fields.
End-to-end exposure in model lifecycle development, including demonstrated experience deploying and maintaining ML models in production environments.
Strong knowledge of LLM APIs, prompt engineering, and conversational AI patterns.
Experience in fine-tuning LLMs for domain-specific enterprise applications.
Experience with MLOps and LLMOps, ensuring scalable, reliable, and monitorable model deployments.
Worked with agentic frameworks and autonomous agent architectures.
Proficiency in Python and modern software development practices (testing, code review, CI/CD).

Benefits

Comp & perks

Competitive salary
Flexible working hours
Professional development budget
Home office setup allowance
Global team events