Senior Forward Deployed Engineer, AI Infrastructure & Deployments

Responsibilities

• 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.

Requirements

• 7+ years of software engineering experience, with a track record of building and owning systems that run in production.
• Hands-on experience building LLM-powered applications, agentic workflows, or AI tools beyond prototypes.
• Strong AWS and Terraform experience, including VPC networking, IAM, security controls, and standing up reliable customer or single-tenant environments.
• Strong Python skills, experience with APIs, async service patterns, and building software that other teams, customers, or businesses depend on.
• 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.
• Comfort designing for data isolation, customer security requirements, and regulated environments with constraints such as SOC 2, BAA, HIPAA, or similar considerations.
• Experience building integration surfaces for AI systems, including MCP servers or equivalent patterns that connect tools, data, and workflows.
• Ability to lead technical conversations with customer engineers and senior stakeholders, explain tradeoffs clearly, drive alignment, and build trust without handing off the conversation.
• 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.

Nice-to-Haves

• Experience with LLM evaluation frameworks in production, such as LangSmith, Braintrust, Ragas, or equivalent tools.
• Familiarity with healthcare or life sciences data, including IQVIA data structures, pharma customer workflows, payer data contracts, or similar.