Senior Forward Deployed Engineer, AI

About the role

As a Senior Forward Deployed Engineer, AI, you will own end-to-end delivery for some of Komodo’s most technically demanding customer engagements — from solution architecture through deployment inside a customer’s cloud, data infrastructure, and compliance environment.

This is a deeply technical engineering role with direct customer context. You will write production code, design cloud-native deployment patterns, build integrations, develop MCP servers that extend the Komodo platform into a customer’s stack, and debug issues in environments you do not fully control.

The Forward Deployed Engineering team

The Forward Deployed Engineering team operates with a clear principle: own your work end to end. You will be expected to translate customer requirements into scalable, product-adjacent solutions, communicate clearly with technical and non-technical stakeholders, surface risks early, and make sound tradeoffs between speed and production rigor.

Essential job duties

• Design, build, and deploy AI-native solutions inside customer environments, including MCP servers, agentic workflows, and custom integrations that adapt Komodo’s platform to each customer’s cloud infrastructure, systems, data contracts, and compliance requirements.
• Own the infrastructure layer for customer deployments, including Terraform-managed AWS environments, VPC networking, IAM, security controls, and data isolation requirements.
• Work with data at scale across tools like Databricks, Delta Lake, Snowflake, and S3 — debugging pipeline failures, optimizing performance, and building integrations that hold up in production.
• Drive day-to-day technical engagement with customer stakeholders by scoping work, communicating progress, explaining tradeoffs, surfacing risks early, and building trust over time.
• Turn field learnings into reusable FDE patterns, deployment templates, engineering standards, and roadmap input for Core Platform and architecture governance.

What you bring to Komodo Health

• Production engineering experience: 7+ years of software engineering experience, with a track record of building and owning systems that run in production.
• Agentic AI experience: Hands-on experience building LLM-powered applications, agentic workflows, or AI tools beyond prototypes. Experience with frameworks such as LangGraph, Strands, CrewAI, or similar is a plus.
• Cloud infrastructure ownership: Strong AWS and Terraform experience, including VPC networking, IAM, security controls, and standing up reliable customer or single-tenant environments.
• Application engineering depth: Strong Python skills, experience with APIs, async service patterns, and building software that other teams, customers, or businesses depend on.
• Data platform experience: Experience working with large-scale data systems such as Databricks, Delta Lake, Snowflake, S3, or similar platforms, including debugging failures and improving performance or reliability.
• Security and enterprise judgment: Comfort designing for data isolation, customer security requirements, and regulated environments with constraints such as SOC 2, BAA, HIPAA, or similar considerations.
• Integration mindset: Experience building integration surfaces for AI systems, including MCP servers or equivalent patterns that connect tools, data, and workflows.
• Customer-facing technical leadership: Ability to lead technical conversations with customer engineers and senior stakeholders, explain tradeoffs clearly, drive alignment, and build trust without handing off the conversation.
• Comfort with ambiguity: Ability to work across infrastructure, application, data, and AI layers, find a path when one does not already exist, and deliver on what you commit to.