Job Description

We’re growing our Data Science team to drive innovation in Generative AI. As a Data Scientist – Applied AI & Prompt Engineering, you will design, build, and deploy LLM-powered solutions that directly impact our products and users. You’ll work hands-on with LLMs, transformers, retrieval-augmented generation (RAG), and AI agents, collaborating with Engineering to bring prototypes all the way to production and take ownership of their ongoing maintenance, enhancement, and evolution.

What You’ll Do

• Architect and implement production-ready AI solutions involving LLMs, transformer-based models, retrieval systems, agentic workflows, and AI agents for generative tasks and automation.

• Design and iterate on prompts, workflows, and RAG pipelines to improve accuracy, cost-efficiency, latency, and safety.

• Design and build multi-step agentic systems that break down complex tasks, invoke external tools or APIs, manage state, and handle reasoning chains robustly.

• Deploy models and GenAI pipelines in production environments (API, batch, streaming), ensuring reliability and scalability.

• Build and maintain evaluation frameworks to measure model grounding, factuality, latency, and cost.

• Develop and integrate guardrails (e.g., prompt-injection protections, content moderation, output validation), and safeguards for agent loops (e.g., loop prevention, tool call limits, state validation).

• Collaborate cross-functionally with Product, Engineering, and ML Ops to deliver high-quality AI features end-to-end.

Qualifications

Experience: 3+ years applied machine learning, with hands-on focus on NLP, transformers, or generative AI systems.

LLM and Agent Tools: Hands-on experience with LLM-related libraries (e.g. LangChain, LlamaIndex, OpenAI API, CrewAI, or similar) and services (Azure Prompt flow, AWS Bedrock agents, or similar)

Agentic Systems: Experience designing multi-step agents that combine LLM reasoning with tool/API calls, with safeguards against errors, loops, and unsafe tool use.

ML Foundations: Proven experience building and deploying machine learning models to production (API, batch, or streaming).

Coding: Fluency in Python, with clean, modular, production-grade code practices.

Experimentation: Strong ability to design and analyze ML experiments; track performance using metrics, not gut feel.

Deployment: Ability to develop, deploy and monitor AI-powered applications in cloud environments (e.g. AWS, Azure, GCP) using APIs, batch, or streaming architectures. Familiarity with containerization, versioning, and CI/CD.

Responsible AI: Experience implementing privacy, bias mitigation, safety guardrails, or related practices.

Qualifications

• Degree in Computer Science, Data Science, Engineering, or a related field (or equivalent experience).

• Expertise in transformer-based models and LLM architectures.

• Ability to bridge rapid prototyping and production deployment — you own what you build through to live systems.

• Strong collaborator who thrives at the intersection of DS + Engineering.