Senior Forward Deployed Engineer, AI Infrastructure & Deployments

Komodo Health • Chicago, IL

12d • $191,000 - $253,000 • Hybrid

About The Position

Komodo’s Labs team builds Marmot — our AI-native product, powered by an MCP Gateway architecture that connects our data platform to the tools customers need. The Forward Deployed Engineering team is where Marmot meets the real world: we deploy AI-native solutions into complex enterprise healthcare environments, including customer cloud infrastructure, strict compliance requirements, and integrations that vary by engagement. We sit at the intersection of Engineering, Product, and Revenue. We build, deploy, and own production outcomes for some of Komodo’s most complex and visible customer engagements. As a Senior Forward Deployed Engineer, AI Infrastructure & Deployments 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 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. This role is best suited for someone who enjoys ambiguity, takes initiative, and is energized by solving complex problems in close partnership with customers. You’ll work on some of Komodo’s most complex and visible deployments — and see your work run in production.

Requirements

• 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.
• 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.

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 data environments.
• Experience with API gateway or service mesh patterns used for AI tool integration.
• Contributions to shared platform infrastructure, including code committed to shared repos, participation in architecture reviews, or tooling that other engineers depend on.
• Experience deploying in regulated environments with data residency, audit logging, or multi-party data access constraints.
• Experience with frameworks such as LangGraph, Strands, CrewAI, or similar is a plus.

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.

Benefits

• comprehensive health, dental, and vision insurance
• flexible time off and holidays
• 401(k) with company match
• disability insurance and life insurance
• leaves of absence in accordance with applicable state and local laws and regulations and company policy

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed