Forward Deployed Engineer - AI

Nexla•San Mateo, CA

3h•$130,000 - $160,000•Hybrid

About The Position

Nexla is the leading Integration platform, built with AI, for AI. Nexla takes a metadata driven approach to converge diverse integrations across Data, Documents, Agents, Applications, and APIs into a single design pattern. We accelerate the development of solutions for GenAI, Analytics, and Inter-company data. Nexla makes data users and developers up to 10x more productive by delivering a true blend of no-code, low-code, and pro-code interfaces. Leading companies including DoorDash, LinkedIn, Johnson & Johnson, and LiveRamp trust Nexla for mission-critical data. Named in the 2022, 2023, and 2024 Gartner Magic Quadrant™ for Data Integration Tools and top-rated by customers on Gartner Peer Insights, Nexla is a remote-first company headquartered in San Mateo, California. At Nexla, our culture is built around our core values: Have Empathy, Be Curious, Be Intellectually Honest, Achieve Excellence, and Remember to Relax. We put our customers at the heart of everything we do, foster a data-driven mindset, take ownership of our work, and believe in the power of teamwork to achieve ambitious goals. Our customers operate in messy, high-stakes data environments, fragmented systems, legacy pipelines, complex compliance requirements, and aggressive timelines. They don't need a demo. They need an engineer who can sit inside their world and make Nexla work for their specific reality. As a Forward Deployed AI Engineer at Nexla, you are that engineer. You'll embed directly with our most strategic customers to understand their data challenges, build solutions on top of the Nexla platform, and get those solutions into production. This is a hands-on, engineering-first role. You'll write code, build integrations, design data flows, and troubleshoot production issues all while developing a deep understanding of each customer's business context. You're not handing off specs. You're shipping outcomes. The insights you bring back from the field directly shape our product. You'll work closely with the CTO, Product, and core Engineering to surface patterns, identify gaps, and influence what we build next. In many ways, this role operates like the technical co-founder of each customer engagement.

Requirements

1+ years of software engineering experience, with a track record of building and deploying production systems. Former technical founders or engineers with consulting experience are a great fit.
Experience building or deploying agentic AI systems in enterprise environments.
Strong Python skills. You're comfortable building data pipelines, writing integrations, working with APIs, and scripting your way through ambiguous problems.
Knowledgeable about enterprise data infrastructure data warehouses (Snowflake, Databricks, BigQuery), streaming platforms (Kafka), ETL/ELT patterns, and the modern data stack.
Hands-on experience with AI/LLM technologies prompt engineering, agent development, orchestration frameworks, or building AI-powered workflows. You don't need to be an ML researcher, but you should be able to build with these tools.
Customer-facing confidence. You can run a technical discovery call, whiteboard an architecture with a customer's data team, and explain trade-offs to non-technical stakeholders without dumbing things down.
High agency. You thrive in ambiguous situations where the problem isn't well-defined and the playbook doesn't exist yet. You figure it out, make decisions, and move.
You've owned outcomes, not just tasks. You've been the person responsible for making something work in a real environment, not just in a staging cluster.

Nice To Haves

Experience at a data integration, iPaaS, or ELT company (Fivetran, MuleSoft, Informatica, Airbyte) or in a customer-facing engineering role.
Familiarity with data protocols and patterns: CDC, GraphQL, gRPC, Webhooks, REST.
Cloud-native experience (AWS, GCP) including working with managed data services, IAM, networking.
Comfort with travel (up to 25%) to embed with customer teams when needed.

Responsibilities

Build & Deploy at Customer Sites (60%)
Embedded with strategic customers to understand their data infrastructure, integration requirements, and business goals then architect and implement solutions using the Nexla platform.
Build production data pipelines, connectors, and workflows tailored to each customer's environment. This includes working across their data warehouses, SaaS apps, streaming systems, and internal APIs.
Design and deploy AI-powered data flows leveraging Nexla's intelligent connectors, context-aware transformations, and agentic capabilities to solve problems that traditional integration tools can't.
Own the technical delivery end-to-end. You scope the work, build it, test it, deploy it, and ensure it runs reliably. When something breaks at the customer site, you're the first responder.
Navigate complex enterprise data environments - messy schemas, undocumented APIs, legacy systems, regulatory constraints. You figure it out and make it work.
Feed the Product (25%)
Identify repeatable patterns across customer engagements. What you build for one customer should inform what we build into the platform for everyone.
Surface product gaps, UX friction, and missing capabilities directly to the Product and Engineering teams. You're our best source of ground truth on what customers actually need.
Contribute back to the Nexla platform build reusable connectors, templates, and reference architectures that accelerate future deployments.
Write the deployment playbooks, integration guides, and best practices that make the next engagement faster and smoother.
Partner with Go-to-Market (15%)
Support Sales during high-stakes technical evaluations. Help prospective customers see what's possible by scoping solutions and building proof-of-value demonstrations.
Act as the trusted technical partner for customer stakeholders from individual data engineers to VPs of Data. You translate between their world and ours.
Collaborate with Customer Success to ensure long-term adoption and expansion. The deployment doesn't end when the pipeline goes live.