Staff Forward Deployed Engineer

afresh; United States; Remote; USD 150,000 - 190,000; Full time

Benefits offered by this job

Medical, dental, and vision coverage

Equity for eligible U.S. employees

401(k) with company matching

Home-office support

Coworking allowances

Professional development funding

Wellness stipend

Telecommunications stipend

Flexible paid time off

Job summary

afresh seeks a staff-level engineer to blend customer-embedded delivery with platform development, focusing on integrating AI into complex enterprise data environments and building production-grade pipelines.

You will switch between architecture, implementation, customer collaboration, and platform ownership, turning field lessons into reusable infrastructure for future deployments.

Qualifications

• 5+ years of experience building production software and data systems.
• Ability to turn ambiguous requirements and messy data landscapes into workable architectures.
• Demonstrated experience building real-world LLM or agent systems using retrieval, RAG, or tool-use patterns.
• Solid data-engineering experience, including production pipelines and modern cloud data platforms such as Databricks, BigQuery or Snowflake.
• Comfort working directly with customer engineering and data teams through technical workshops, explanations, and delivery ownership.
• Ability to move effectively between customer-facing work and focused platform engineering.

Responsibilities

• Work with customer technical teams to define data sources, architecture, delivery scope, and the path from concept to production.
• Integrate with cloud and data platforms, build reliable pipelines, and transform inconsistent enterprise data into trustworthy data products.
• Design and ship production-ready LLM and agent systems, including retrieval, tool use, agentic workflows, data-quality agents, and analytics agents.
• Develop shared knowledge and grounding infrastructure such as knowledge graphs, ontologies, retrieval layers, agent frameworks, and serving systems.
• Build evaluation, tracing, and operational tooling to measure accuracy, latency, cost, and overall system quality.
• Replace one-off customer implementations with clean interfaces and reusable components for future deployments.

Skills

Data pipelines

LLM/agent systems

Cloud data platforms

Customer collaboration

Platform ownership

Production software

Tools

Databricks

BigQuery

Snowflake

Job description

Role overview

Staff-level engineer who alternates between customer-embedded delivery and platform development. The role involves integrating AI systems into complex enterprise data environments, building production-grade pipelines and data products, then turning field lessons into reusable infrastructure for future deployments. This is hands-on, ambiguous 0-to-1 work suited to someone comfortable switching between architecture, implementation, customer collaboration, and platform ownership.

Responsibilities

• Work with customer technical teams to define data sources, architecture, delivery scope, and the path from initial concept to production.
• Integrate with cloud and data platforms, build reliable pipelines, and transform inconsistent enterprise data into trustworthy data products.
• Design and ship production-ready LLM and agent systems, including retrieval, tool use, agentic workflows, data-quality agents, and analytics agents.
• Develop shared knowledge and grounding infrastructure such as knowledge graphs, ontologies, retrieval layers, agent frameworks, and serving systems.
• Build evaluation, tracing, and operational tooling to measure accuracy, hallucination rates, latency, cost, and overall system quality.
• Replace one-off customer implementations with clean interfaces and reusable components that improve future deployments.

Requirements

• At least five years of experience building production software and data systems.
• Strong engineering judgment and the ability to turn ambiguous requirements and messy data landscapes into workable architectures.
• Demonstrated experience building real-world LLM or agent systems using retrieval, RAG, or tool-use patterns.
• Solid data-engineering experience, including production pipelines, enterprise data modeling, and modern cloud data platforms such as Databricks, BigQuery, Snowflake, or similar.
• Comfort working directly with customer engineering and data teams through technical workshops, explanations, and delivery ownership.
• Ability to move effectively between customer-facing work and focused platform engineering.

Benefits and work setup

• Remote work is available for employees residing in specified U.S. states, with some customer work potentially requiring on-site engagement.
• Medical, dental, and vision coverage, with substantial premium support, plus mental health services.
• Competitive compensation, equity for eligible U.S. employees, and a 401(k) program with company matching.
• Home-office support, coworking allowances, professional development funding, wellness and telecommunications stipends, and flexible paid time off.