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Precisely is the leader in data integrity. We empower businesses to make more confident decisions based on trusted data through a unique combination of software, data enrichment products and strategic services. What does this mean to you? For starters, it means joining a company focused on delivering outstanding innovation and support that helps customers increase revenue, lower costs and reduce risk. In fact, Precisely powers better decisions for more than 12,000 global organizations, including 95 of the Fortune 100. Precisely's 2500 employees are unified by four company core values that are central to who we are and how we operate: Openness, Determination, Individuality, and Collaboration. We are committed to career development for our employees and offer opportunities for growth, learning and building community. With a "work from anywhere" culture, we celebrate diversity in a distributed environment with a presence in 30 countries as well as 20 offices in over 5 continents. Learn more about why it's an exciting time to join Precisely!
Precisely is an AI-first organization. All employees are expected to demonstrate proficiency in applying AI tools to accelerate their work, improve output quality, and eliminate low-value tasks. Candidates should be comfortable using generative AI tools (e.g., Microsoft Copilot, ChatGPT) in their day-to-day workflows, able to evaluate AI-generated outputs critically, and open to continuously adopting new AI capabilities as they emerge.
Overview: Product Operations at Precisely is building the operating system that runs the Product organization. We are moving the Product org toward AI agents that plan, spec, research, query, and act. This is the engine behind our Product Operating System (PM-OS). We are looking for a Product Intelligence Engineer who can build and maintain this PM-OS, including agents end to end. You will design, build, test, and operate the PM-OS that Product Managers and leadership rely on every day, from conversational analytics to agentic Product Development Lifecycle, which encompasses multi-step agents that run on a schedule and push outputs without human intervention. It takes business and product judgment: an agent is only useful if it understands the question behind the question, hence you will need to grasp how a software company runs (product, portfolio, renewals, go-to-market) and builds products well enough to build agents that are accurate and trusted.
What you will do:
- Design and build AI agents. Own agent architecture end to end: personas, decision logic, tool and function calling, orchestration, and memory and context management. Build agents that range from simple PM-facing Q&A to scheduled, multi-step agents that query live data and push results to Teams and SharePoint with no manual work.
- Translate business problems into agent solutions. Work directly with PMs and leadership to turn ambiguous business questions into executable agent workflows, and deliver insights that drive real decisions.
- Integrate agents with our systems. Connect agents to GitHub, Snowflake, Linear, Salesforce, Tableau, internal APIs, and MCP servers so they can retrieve governed data and take real actions.
- Engineer prompts and workflows. Develop robust prompting strategies and multi-agent workflows that produce reliable, high-quality, repeatable outputs.
- Own the semantic layer. Define and maintain the metric definitions, calculated fields, and data-model documentation that sit between the Snowflake warehouse and the agent layer. This is what makes agents accurate and trustworthy.
- Test, validate, and monitor in production. Run scenario and edge-case testing, check outputs against canonical data sources, and stand up observability, versioning, and accuracy monitoring once agents are live.
- Lead the migration from BI to conversational. Retire Tableau dashboards and replace them with agent-based equivalents, deciding what to rebuild, retire, or simplify based on real business use.
- Enable Product Managers. Build starter prompts, onboarding materials, and embedded workflows so agents are useful to people who are not data specialists.
What we are looking for:
- Hands-on agent development (core requirement). You have built LLM-based agents, not just used a chatbot. Practical experience with the Anthropic API (Claude) or equivalent, function calling, RAG, vector stores, orchestration frameworks (for example LangChain, LlamaIndex, AutoGen, or CrewAI), and MCP. You have shipped agents that run reliably.
- Engineering and coding (core requirement). Proficiency in Python (TypeScript or JavaScript a plus) to build agentic workflows, integrate systems, and maintain production-grade code. You build and own coded agents end to end.
- Data foundations. Solid Data Warehouse and SQL: writing SQL, navigating schemas, modeling data, and governing metric definitions. Experience with Snowflake Cortex or similar AI-on-warehouse tooling is a strong plus.
- Business understanding. You can translate a business question into a data model and an agent. You understand how a software business works and what data actually answers a leadership question. Experience in product or data analytics in a software environment is a strong plus, and it is what separates a useful agent from a merely working one.
- Strong English communication. You will present findings and recommendations to senior product and leadership stakeholders in English. Business-level English is required.
- Autonomy. This team moves fast and runs with high autonomy. You make judgment calls, flag blockers early, and deliver without close supervision. Experience in a remote or distributed team is a plus.
- Bachelor's degree in Computer Science, Data Engineering, Information Systems, or a related technical field. Equivalent hands-on professional experience in AI/ML engineering or data engineering will be accepted in place of a formal degree.
- 3–5 years of relevant professional industry experience in software/data engineering, AI development, or a closely related role. We will also consider strong candidates from data analytics backgrounds (e.g. Product Analytics, Business Controlling) who demonstrate solid, hands-on understanding of AI agent development, LLM tooling, and AI workflows. For these candidates, the depth of AI/data skills and business judgment will be weighted over formal engineering tenure.
AI Skills/Knowledge:
- Designs, builds, and operates production AI agents for data querying, reporting, and workflow automation.
- Engineers prompts and multi-agent workflows for reliable, governed outputs.
- Designs and maintains semantic layers so data definitions stay consistent and trustworthy across tools and agents.
- Integrates LLMs with enterprise systems through APIs, function calling, and MCP.
- Applies AI/ML evaluation and output-validation methods to keep agents accurate.
- Automates repetitive workflows and synthesizes cross-functional data into executive-ready outputs.
- Contributes to the data-quality standards that underpin AI reliability.
Preferred Skills:
- Familiarity with Linear, ERP systems, Salesforce or Snowflake data structures given our primary data sources.
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