Senior Forward Deployed Engineer

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

Benefits offered by this job

Medical, dental, vision coverage

Retirement matching

Equity

Home-office and coworking support

Professional development funding

Monthly wellness stipend

Job summary

afresh is seeking a Senior Forward Deployed Engineer to blend customer-facing delivery with platform engineering. You will partner with enterprise grocery clients to integrate complex data, build production-grade AI systems, and translate deployments into reusable infrastructure and tooling that accelerates future projects.

You will lead architecture discussions, design data pipelines, and implement retrieval-augmented generation, tool use, and evaluation workflows.

Qualifications

• At least 3 years of experience building production software and data systems.
• Ability to turn ambiguous problems and complex data environments into practical architectures.
• Hands-on experience with real LLM or agent applications, including retrieval-augmented generation.
• Strong data-engineering experience with production pipelines and cloud data platforms such as Databricks, BigQuery, Snowflake.
• Comfort switching between customer-facing collaboration and focused platform development.

Responsibilities

• Collaborate with customer technical teams to define scope and design architecture.
• Integrate with cloud and data platforms, build reliable pipelines, and clean data.
• Design and deploy production-grade LLM and agent systems with retrieval and tooling.
• Strengthen platform capabilities like knowledge graphs, ontologies, and serving infra.
• Build observability tools to measure accuracy, latency, and cost.
• Create reusable interfaces and components while feeding learnings back to the platform.

Job description

Role overview

The Senior Forward Deployed Engineer combines customer-facing delivery with platform engineering. You will work directly with enterprise grocery organizations to integrate complex data, build production-grade AI systems, and then turn lessons from those deployments into reusable infrastructure, evaluation tools, and frameworks that accelerate future implementations.

Responsibilities

• Work with customer technical teams to define scope, design architecture, identify data sources, and establish a path to production.
• Integrate with cloud and data platforms, build reliable pipelines, and transform messy enterprise data into trustworthy data products.
• Design and deploy production-ready LLM and agent systems, including retrieval, tool use, data-quality workflows, and analytics agents.
• Strengthen shared platform capabilities such as knowledge graphs, ontologies, retrieval and grounding layers, agent frameworks, and serving infrastructure.
• Build evaluation, tracing, and observability tools to measure accuracy, hallucination rates, latency, cost, and system quality.
• Create clean interfaces and reusable components instead of one-off customer implementations, while feeding field learnings back into the platform.

Requirements

• At least 3 years of experience building production software and data systems with strong engineering practices.
• Ability to turn ambiguous problems and complex data environments into practical architectures and working systems.
• Hands-on experience building real LLM or agent applications, including retrieval-augmented generation, tool use, and quality evaluation.
• Strong data-engineering experience with production pipelines, enterprise data modeling, and modern cloud data platforms such as Databricks, BigQuery, Snowflake, or similar.
• Comfort switching between customer-facing collaboration and focused platform development.
• Ability to lead technical working sessions, explain design decisions, and build trust through delivery.

Benefits and work setup

• Remote work is available in specified U.S. states, with some customer work conducted remotely or on site.
• Listed benefits for eligible full-time U.S. employees include medical, dental, and vision coverage, mental-health support, retirement matching, equity, home-office and coworking support, professional-development funding, monthly wellness and telecommunications stipends, and flexible paid time off.