Note: The job is a remote job and is open to candidates in USA. phData is a remote-first data and AI consultancy company that partners with industry leaders to solve complex data and AI challenges. The Forward Deployed AI Product Engineer will build AI-native applications and agents, working closely with business stakeholders to identify use cases, prototype solutions, and integrate them into clients' platforms.
Responsibilities
- Identify and shape AI use cases that were previously considered too messy, too manual, or too cross-functional to tackle
- Rapidly prototype full-stack, MVP-grade agents, applications, and workflows that demonstrate value in weeks, not quarters
- Productionize the most impactful solutions, integrating with the client’s AI Data Platform, existing systems, and operational workflows
- Feed technical and architectural patterns back into the client’s platform and operating model to create durable, long-term leverage
- Own the end-to-end lifecycle of AI-native solutions in your business area: discovery, value articulation, design, implementation, iteration, and handoff
- 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
Skills
- 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
- Technologies (experience with several of the following, and the ability to learn others quickly): Frontend: Streamlit, Gradio, React, or similar. Databases and Storage: Postgres, cloud-native warehouses, Vector DBs, Graph DBs
- Workflow and Automation: Cloud orchestration tools, CI/CD pipelines, and AI-native workflow systems; familiarity with tools like Microsoft Copilot or similar is a plus
- Platforms: Snowflake and major cloud providers (AWS, Azure, GCP); experience with modern data and AI platforms and APIs
- Agentic AI Skills: Practical experience with RAG, LLM workflows, agentic frameworks, prompt engineering, and implementing guardrails and evaluations for AI systems
- Curious and Continuous Learner: Demonstrated ability to upskill rapidly on new APIs, data platforms, workforce systems, and emerging AI tooling
- Documentation and Enablement: Contribute to internal and client-facing documentation, run-throughs, demos, and training that help others adopt and extend the solutions you build
- 4+ years of hands-on software engineering experience (more for Senior/Principal levels), including building and operating production systems
- Travel up to 50% (client-dependent). Frequent on-site presence with customers is expected for this role
- BA/BS in computer science, engineering, mathematics, operations, or related fields preferred. Equivalent practical experience considered
- Prior consulting, customer-embedded engineering, or product engineering experience in data/AI-heavy environments strongly preferred
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