Enterprise AI Product Engineer

About the role

Join business meetings and workshops to uncover automation and AI opportunities in real time; translate them into concrete solution architectures on the spot.
Design and recommend solution patterns — RAG pipelines, agentic workflows, MCP integrations, prompt/eval frameworks — choosing the right approach for each use case.
Evaluate third-party AI tools (Copilot, Gemini, Claude, etc.) against custom in-house builds using structured technical and business criteria.
Define data flows, integration points, and system contracts across enterprise platforms such as Salesforce, Workday, NetSuite, and cloud AI services (AWS, GCP, Azure).
Develop working prototypes, proof-of-concepts, and production-grade AI features — not slide decks.
Implement and iterate on RAG pipelines, LLM orchestrations, agentic workflows, API integrations, and chatbot/copilot experiences.
Establish telemetry and LLM evaluation frameworks (correctness, faithfulness, latency, cost, token usage) and monitor live systems post-launch.
Collaborate closely with engineering teams through code reviews, technical workshops, and paired development sessions.
Drive product direction by partnering with business units (Sales, Marketing, HR, Finance, Legal, CX) to identify high-impact use cases, quantify ROI, and define measurable success criteria.
Maintain the product roadmap for AI initiatives, owning quarterly planning, backlog prioritization, and end-to-end AI-DLC from discovery through launch and iteration.
Stay current with the AI tool landscape and comparison matrix spanning general-purpose copilots and function-specific enterprise apps.
Use agile rituals and rapid experimentation to learn quickly and keep delivery momentum.
Serve as the organization’s resident AI practitioner: educate stakeholders on what’s possible, set realistic expectations, and demystify technical concepts for non-technical audiences.
Lead AI training sessions, internal demos, and working sessions to accelerate adoption across business functions.
Monitor and communicate the quantifiable impact of launched AI solutions (time saved, quality, adoption, CSAT).

Responsibilities

• Architect AI Solutions End-to-End
• Develop working prototypes, proof-of-concepts, and production-grade AI features — not slide decks.
• Implement and iterate on RAG pipelines, LLM orchestrations, agentic workflows, API integrations, and chatbot/copilot experiences.
• Establish telemetry and LLM evaluation frameworks (correctness, faithfulness, latency, cost, token usage) and monitor live systems post-launch.
• Collaborate closely with engineering teams through code reviews, technical workshops, and paired development sessions.
• Drive product direction by partnering with business units (Sales, Marketing, HR, Finance, Legal, CX) to identify high-impact use cases, quantify ROI, and define measurable success criteria.
• Maintain the product roadmap for AI initiatives, owning quarterly planning, backlog prioritization, and end-to-end AI-DLC from discovery through launch and iteration.
• Stay current with the AI tool landscape and comparison matrix spanning general-purpose copilots and function-specific enterprise apps.
• Use agile rituals and rapid experimentation to learn quickly and keep delivery momentum.
• Serve as the organization’s resident AI practitioner: educate stakeholders on what’s possible, set realistic expectations, and demystify technical concepts for non-technical audiences.
• Lead AI training sessions, internal demos, and working sessions to accelerate adoption across business functions.
• Monitor and communicate the quantifiable impact of launched AI solutions (time saved, quality, adoption, CSAT).

Requirements

• 3+ years of hands-on experience building or integrating AI-powered solutions in an enterprise setting (RAG, LLM applications, AI agents, or similar).
• Demonstrated ability to architect solutions from scratch — designing data flows, integration patterns, and system components in live stakeholder settings.
• Proficiency in at least one scripting or development language (Python strongly preferred; familiarity with JavaScript/TypeScript a plus).
• Solid grasp of modern AI/ML concepts: LLMs, embeddings, vector databases, prompt engineering, retrieval-augmented generation, and agent frameworks.
• Experience integrating with enterprise APIs and platforms (REST, GraphQL, OAuth 2.0, webhooks) and familiarity with data stores (PostgreSQL, MongoDB, Redis, or similar).
• Strong communication skills — able to explain technical architecture clearly to executives, and to translate fuzzy business problems into precise technical requirements.
• Track record of shipping: prototypes that became products, pilots that became programs.

Nice to Have

• Exposure to enterprise systems: Salesforce, Workday, NetSuite, ServiceNow, or similar HRIS/CRM/ITSM platforms.
• Experience with conversational AI platforms: Google Dialogflow CX, Microsoft Copilot Studio, or equivalent.
• Familiarity with cloud AI/ML services on AWS, GCP, or Azure.
• Background or coursework in product management, systems design, or solutions architecture.
• Comfort with SQL and analytical tooling for evaluating AI system performance.