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About the Role

The AI Engineer (LLM/Agent) will own the conversational layer for ML model outputs, develop a "Revenue Assistant" Agent from R&D to prototype, and design context architecture grounded in client-specific pricing data. This role focuses on building intelligent systems that reason, automate workflows, and augment human decision-making, advancing PureFacts’ AI-first strategy by developing AI-powered copilots, agents, and automation tools.

Responsibilities

Design and build LLM-powered applications and AI agents for internal and client-facing use cases.
Develop AI copilots for internal teams and clients.
Develop intelligent workflow automation agents.
Develop natural language interfaces for data and reporting.
Implement prompt engineering, tool usage, and agent orchestration frameworks.
Identify opportunities to replace manual processes with AI-driven automation.
Build systems that enable users to interact with complex data through natural language.
Develop AI solutions to enhance revenue insights and analytics.
Develop AI solutions to enhance client reporting and communication.
Develop AI solutions to enhance operational efficiency across workflows.
Integrate LLMs into PureFacts’ SaaS platform and data systems.
Build APIs and services to support AI-powered features.
Work with data and engineering teams to ensure secure, scalable, and reliable integrations.
Design and implement RAG pipelines using structured and unstructured data sources.
Work with vector databases (e.g., Pinecone, Weaviate).
Work with embedding models and semantic search.
Ensure accurate, relevant, and context-aware outputs from AI systems.
Develop frameworks to evaluate LLM outputs for quality, accuracy, and reliability.
Continuously optimize prompts, models, and workflows.
Monitor system performance and implement improvements.
Leverage and integrate tools such as OpenAI, Azure OpenAI, or similar LLM providers.
Leverage and integrate tools such as LangChain, LlamaIndex, or agent frameworks.
Leverage and integrate tools such as APIs, microservices, and cloud infrastructure.
Collaborate with MLOps to ensure scalable and maintainable deployments.
Ensure AI solutions are secure, compliant, and aligned with responsible AI principles.
Address data privacy and security.
Address model hallucination and reliability.
Address explainability and transparency.
Partner with Product, Engineering, and Client teams to translate AI capabilities into business value.
Help stakeholders identify opportunities to increase efficiency and reduce manual effort.
Communicate technical concepts in a clear, practical way.

Requirements

1-3 years of LLM application development - RAG pipelines, vector databases, agent orchestration (tool-use, multi-step reasoning).
Experience with evaluation frameworks for generative AI, and in putting guardrails/safety in regulated contexts.
Familiar with agent frameworks (LangGraph or similar).
Hands-on experience building LLM-based applications or AI agents.
Experience in SaaS, fintech, or data-driven environments is preferred.
Strong programming skills in Python.
Experience with LLM APIs (OpenAI, Azure OpenAI, Anthropic, etc.).
Experience with prompt engineering and agent frameworks (LangChain, LlamaIndex, etc.).
Experience with APIs and microservices architecture.
Experience with data processing (SQL, Python data libraries).
Familiarity with vector databases and embeddings.
Familiarity with cloud platforms (AWS, Azure, GCP).
Experience building Retrieval-Augmented Generation (RAG) systems.
Experience building multi-step agent workflows.
Experience building tool-using agents and automation systems.
Strong understanding of LLM limitations and optimization techniques.
Strong understanding of evaluation methods for generative AI.
Passion for using AI to automate workflows and eliminate low-value work.
Ability to translate AI capabilities into practical, high-impact solutions.
Strong focus on user experience and real-world application.
Ability to work across technical and non-technical teams.
Strong problem-solving and systems thinking skills.
Clear communication of complex AI concepts.

Skills

LLM application development
RAG pipelines
Vector databases
Agent orchestration
Prompt engineering
Python
LLM APIs
APIs
Microservices architecture
Data processing
SQL
Embeddings
Cloud platforms
Retrieval-Augmented Generation (RAG)
Multi-step agent workflows
Tool-using agents
Automation systems
Systems thinking

Experience Level

1-3 years

Education Level

Degree in Computer Science, Engineering, Data Science, or related field
Advanced degree is a plus but not required

About the Company

Purefacts is advancing an AI-first strategy.