Forward Deployed AI Product Engineer

phData is a dynamic and innovative leader in the modern data stack, partnering with major cloud data platforms to deliver cutting-edge services. The Forward Deployed AI Product Engineer will build AI-native applications and agents that solve high-value business problems, working closely with business stakeholders to rapidly prototype and productionize impactful solutions.

Responsibilities:

• Rapid Prototyping: Design and build full-stack, MVP-grade prototypes for AI-native applications, agents, and workflows that can be validated quickly with end users
• Full-Stack Execution: Deliver end-to-end solutions using a mix of Python, SQL, and TypeScript/JavaScript, including APIs, back-end services, and front-end experiences
• AI Agents and Workflows: Implement AI-powered workflows and agents that solve real business problems, including RAG pipelines, AI agents, workflows, skills, memory, and model-context protocols where appropriate
• User Interfaces: Build high-fidelity, production-ready user interfaces (e.g., Streamlit/Gradio/React or similar) that make AI capabilities usable and intuitive for business users
• Data Transformation/Manipulation: Move, transform, and harmonize disparate data sources into the client’s data platform (e.g., Snowflake, Databricks, cloud data warehouses), exposing clean, reliable data to applications and agents
• Pattern Feeding: Capture and contribute technical patterns, best practices, and anti-patterns back into the client’s platform and phData’s internal playbooks to build long-term leverage
• Platform Advocacy: Bridge the gap between business-unit needs and central platform capabilities. Identify capability gaps (CI/CD, orchestration, observability, streaming ingestion, vector search, AI gateways, etc.) and help prioritize their development
• Pull-Through Identification: Identify new data sources, platform capabilities, and integration opportunities that create demand for additional phData services (data engineering, ML engineering, Analytics, Workforce AI, platform operations)
• Use Case Discovery: Lead or support discovery sessions with business stakeholders to surface high-impact AI opportunities, unconstrained by legacy processes or 'the way we’ve always done it.'
• Value Articulation: Define success metrics in terms of business KPIs and quantify measurable ROI to justify additional services and platform investments
• Operational Empathy: Spend significant time on-site with end users and operational teams to deeply understand workflows, constraints, and edge cases; design solutions that fit real-world usage
• Client-Facing Communication: Engage confidently with executives, managers, and technical teams. Clearly communicate trade-offs, timelines, and risks, and maintain trust while moving quickly

Requirements:

• Design and build full-stack, MVP-grade prototypes for AI-native applications, agents, and workflows that can be validated quickly with end users
• Deliver end-to-end solutions using a mix of Python, SQL, and TypeScript/JavaScript, including APIs, back-end services, and front-end experiences
• Implement AI-powered workflows and agents that solve real business problems, including RAG pipelines, AI agents, workflows, skills, memory, and model-context protocols where appropriate
• Build high-fidelity, production-ready user interfaces (e.g., Streamlit/Gradio/React or similar) that make AI capabilities usable and intuitive for business users
• Move, transform, and harmonize disparate data sources into the client's data platform (e.g., Snowflake, Databricks, cloud data warehouses), exposing clean, reliable data to applications and agents
• Capture and contribute technical patterns, best practices, and anti-patterns back into the client's platform and phData's internal playbooks to build long-term leverage
• Bridge the gap between business-unit needs and central platform capabilities. Identify capability gaps (CI/CD, orchestration, observability, streaming ingestion, vector search, AI gateways, etc.) and help prioritize their development
• Identify new data sources, platform capabilities, and integration opportunities that create demand for additional phData services (data engineering, ML engineering, Analytics, Workforce AI, platform operations)
• Lead or support discovery sessions with business stakeholders to surface high-impact AI opportunities, unconstrained by legacy processes or 'the way we've always done it.'
• Define success metrics in terms of business KPIs and quantify measurable ROI to justify additional services and platform investments
• Spend significant time on-site with end users and operational teams to deeply understand workflows, constraints, and edge cases; design solutions that fit real-world usage
• Engage confidently with executives, managers, and technical teams. Clearly communicate trade-offs, timelines, and risks, and maintain trust while moving quickly
• Experience with several frontend technologies (Streamlit, Gradio, React, or similar)
• Experience with databases and storage (Postgres, cloud-native warehouses, Vector DBs, Graph DBs)
• Experience with cloud orchestration tools, CI/CD pipelines, and AI-native workflow systems; familiarity with tools like Microsoft Copilot or similar is a plus
• Experience with Snowflake and major cloud providers (AWS, Azure, GCP); experience with modern data and AI platforms and APIs
• Practical experience with RAG, LLM workflows, agentic frameworks, prompt engineering, and implementing guardrails and evaluations for AI systems
• Demonstrated ability to upskill rapidly on new APIs, data platforms, workforce systems, and emerging AI tooling
• Contribute to internal and client-facing documentation, run-throughs, demos, and training that help others adopt and extend the solutions you build
• BA/BS in computer science, engineering, mathematics, operations, or related fields preferred. Equivalent practical experience considered
• 4+ years of hands-on software engineering experience (more for Senior/Principal levels), including building and operating production systems
• Prior consulting, customer-embedded engineering, or product engineering experience in data/AI-heavy environments strongly preferred
• Travel up to 50% (client-dependent). Frequent on-site presence with customers is expected for this role