Senior AI Engineer, Komodo Health

• We know strong candidates may not have every tool listed below. We’re most interested in engineers who have shipped production systems, learn quickly, and can operate across AI, infrastructure, data, and customer-facing technical work
• 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
• This role does not require regular travel. Some high-complexity deployments may benefit from occasional on-site customer engagement to build trust, align stakeholders, and strengthen long-term partnerships

Desirable

• 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 AI Engineer, Forward Deployed, 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
• In your first 90 days, you will have:
• Learned the Marmot platform end to end, including the agent loop, MCP Gateway integration surface, and deployment toolchain that allows FDE to stand up isolated customer environments independently
• Shipped your first customer-facing deliverable, such as a new MCP server, Databricks/Delta Lake integration, or agentic workflow — and seen it run in a real customer environment
• Built strong working relationships with Sales, Solutions Engineering, and Product partners, with a clear understanding of who owns what and where you create leverage
• Mapped the technical gaps in your first customer deployment and proposed a concrete plan to close them
• By the end of your first year, you will have:
• Led end-to-end delivery of at least one high-complexity customer deployment, standing up AI-native solutions inside a customer’s own cloud environment and delivering outcomes with measurable business impact
• Built and shipped production-grade agentic workflows and MCP servers that solve customer-specific problems
• Established yourself as a trusted technical partner to senior customer stakeholders by leading architecture reviews, navigating compliance constraints, and helping convert pilots into broader programs
• Contributed reusable deployment patterns to the FDE playbook, creating frameworks the team can rely on for future customer engagements
• Participated in Komodo’s architecture governance, influencing platform decisions based on what you have seen and built in the field
• Partnered cross-functionally with Sales, Product, Data, and Core Engineering to close gaps and shape the roadmap
• 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