Principal GenAI Engineer
AnaplanManchester, United KingdomPosted 12 March 2026
Tech Stack
Job Description
At Anaplan, we are a team of innovators focused on optimizing business decision-making through our leading AI-infused scenario planning and analysis platform so our customers can outpace their competition and the market.
What unites Anaplanners across teams and geographies is our collective commitment to our customers’ success and to our Winning Culture.
Our customers rank among the who’s who in the Fortune 50. Coca-Cola, LinkedIn, Adobe, LVMH and Bayer are just a few of the 2,400+ global companies who rely on our best-in-class platform.
Our Winning Culture is the engine that drives our teams of innovators. We champion diversity of thought and ideas, we behave like leaders regardless of title, we are committed to achieving ambitious goals, and we love celebrating
our wins – big and small.
Supported by operating principles of being strategy-led, values -based and disciplined in execution, you’ll be inspired, connected, developed and rewarded here. Everything that makes you unique is welcome; join us and let’s build what’s next - together!
We're seeking a versatile Principal
GenAI Engineer
who can work across the full stack of GenAI applications, from model integration and prompt engineering to building intuitive user interfaces. You'll build production-ready AI features that empower business users to leverage the power of GenAI within their planning workflows, requiring both deep ML knowledge and strong software engineering skills.
This role requires you to be onsite in our Manchester, UK office twice a week.
Your Impact
Develop end-to-end GenAI features, including backend API services, model integration, model monitoring, evaluations, and deployments
Integrate and optimize LLMs for specific use cases in business planning, including prompt engineering and RAG implementation
Build 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 behaviour
Collaborate with data scientists to productionize ML models and forecasting algorithms
Write comprehensive tests, including unit tests, integration tests, and prompt regression suites
Participate in code reviews, technical design discussions, and knowledge-sharing sessions
Stay current with GenAI research and tools, evaluating new models and techniques for potential adoption
Your Qualifications
Software engineering experience with
a
focus
on ML/AI systems
Strong programming skills in Python, including experience with ML frameworks (PyTorch, TensorFlow, Transformers)
Experience building and deploying LLM-powered applications in production
Proficiency in front-end development with React, TypeScript, and modern web technologies
Understanding of RESTful API design, microservices architecture, and cloud infrastructure
Experience with prompt engineering, RAG systems
Strong foundation in ML fundamentals, including NLP, time-series analysis, or recommender systems
Familiarity with containerization (Docker), orchestration (Kubernetes), and CI/CD pipelines
Excellent problem-solving skills and attention to detail
Bachelor's degree in computer science, Machine Learning, or related field
Preferred Qualifications
Experience with GenAI frameworks (LangChain, LlamaIndex, Haystack, or similar)
Knowledge of vector databases (Pinecone, Weaviate, Qdrant) and embedding models
Experience with model serving frameworks (vLLM, TensorRT, Ray)
Background in forecasting, planning, or analytics applications
Familiarity with Anaplan or similar enterprise planning platforms
Experie ... (truncated, view full listing at source)
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