Role Description
Forward Deployed Engineer — Dynamo AI -----------------------------------------
Location: Remote (East Coast US or United Kingdom)
Compensation: $150,000 – $200,000 base + equity
Experience Level: 3–8 years
Employment Type: Full-Time
About Dynamo AI
Dynamo AI helps enterprises deploy AI systems that are reliable, secure, observable, scalable, and production-ready. The company provides AI evaluation, observability, governance, and guardrail infrastructure for banks, insurance companies, government agencies, and other regulated enterprises. Products include DynamoEval, DynamoGuard, and AgentWarden.
About the Role
This is a customer-facing infrastructure engineering role owning 3–4 enterprise deployments simultaneously. You will deploy and operationalize AI systems in production environments, integrate Dynamo AI products into customer infrastructure, and bridge customer needs with product and engineering teams. The role requires deep Kubernetes and DevOps expertise with strong customer-facing ownership.
What You'll Own
- Deploy and operationalize AI systems in production environments
- Design enterprise deployment architectures
- Integrate Dynamo AI products into customer infrastructure
- Debug and troubleshoot deployment challenges
- Work directly with engineering, security, compliance, and infrastructure teams
- Translate customer deployment challenges into product feedback
- Support AI governance, evaluation, and observability workflows
- Own 3–4 customer deployments simultaneously
- Bridge customer needs with product and engineering teams
Requirements
- 3–8 years of post-undergraduate experience
- Strong Kubernetes experience and networking expertise
- DevOps and platform engineering experience
- Distributed systems knowledge and API integration experience
- Enterprise deployment experience with strong scripting skills
- Customer-facing engineering experience
- Ability to work independently
- Comfortable supporting East Coast US or UK customers
- Available for occasional evening collaboration with India-based teams
Target Backgrounds
Strong signals include Forward Deployed Engineer, Solutions Engineer, Implementation Engineer, AI infrastructure startup experience, Kubernetes production ownership, DevOps leadership, enterprise deployment experience, and regulated industry (financial services, healthcare, government) experience. Ideal roles: Forward Deployed Engineer, Solutions Engineer, Infrastructure Engineer, Platform Engineer, DevOps Engineer, SRE, AI Infrastructure Engineer, Technical Implementation Engineer, or Enterprise Solutions Architect.
Nice to Have
- Master's degree in Computer Science or Engineering
- Generative AI infrastructure or LLM deployment experience
- Financial services, healthcare, or government technology experience
- AI observability, evaluation frameworks, guardrails, or governance and compliance systems
This Role Is NOT For
- Traditional bank DevOps engineers or legacy enterprise IT backgrounds
- Pure software engineers with no deployment experience
- Candidates without customer-facing experience
- Big-company-only backgrounds without startup adaptation
- Weak Kubernetes knowledge or limited infrastructure ownership
Interview Process
- Initial screen with Oliver Liu
- Round 1 technical with Yash — Kubernetes, networking, DevOps
- Round 2 mock customer deployment with Edwin — live Kubernetes deployment exercise, debugging and adaptation; AI tools allowed
- Round 3 domain expertise and behavioral
- Founder round
- Offer
Logistics
- Role is fully remote — East Coast US or United Kingdom
- Occasional evening availability required for collaboration with India-based teams
Shortlisted candidates will be contacted by David Joseph & Co., the recruiting partner managing this search on behalf of Dynamo AI.
About Forward Deployed Engineering
Forward Deployed Engineers are embedded directly with customers to build custom solutions, integrate products into existing infrastructure, and bridge the gap between product engineering and customer success. The role combines deep technical skills with the ability to operate in client environments and translate business requirements into working software.
Originally pioneered by Palantir, the FDE model has spread across AI, enterprise SaaS, and cloud infrastructure companies. FDEs write production code, architect integrations, train customer teams, and feed product insights back to the core engineering organization. At companies like OpenAI, Salesforce, and Databricks, FDE teams are treated as elite engineering units that can ship custom solutions in days rather than quarters.
Typical FDE stack: Python, TypeScript, SQL, REST/GraphQL APIs, cloud platforms (AWS/GCP/Azure), and increasingly LLM APIs and AI orchestration frameworks. Strong communication and the ability to context-switch between technical and business conversations are as important as coding ability.
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