Forward Deployed Engineer

CloudFactory•Seattle, WA

About The Position

CloudFactory is an AI enablement company whose software platform supports trusted AI at scale through services such as data preprocessing, AI model fine-tuning, inference oversight, and MLOps. The company also provides forward deployed engineering services to enterprises that want to use AI but need support designing, proving, developing, deploying, scaling, and operating AI solutions. The Forward Deployed Engineer works directly with strategic clients to design and implement scalable technical integrations, define production-ready workflows, and move AI systems from experimentation to reliable deployment. This is a hands-on engineering position with significant product and client-facing responsibilities. The preferred locations are Dallas, Boston, New York, Seattle, and Chicago. Relocation assistance is available for qualified candidates, and frequent travel to client sites may be required depending on client needs.

Requirements

At least 2 years of software engineering experience.
Strong proficiency in Python and Go.
Proficiency with Claude and relevant AI tools.
Experience with cloud infrastructure such as AWS, GCP, or Azure.
Experience building APIs and working with distributed systems.
Experience designing data pipelines or ML infrastructure.
Ability to evaluate end-to-end workflow performance.
Familiarity with the AI/ML lifecycle, including training, inference, evaluation, and feedback.
Ability to distinguish scalable feature opportunities from custom requests.
Strong prioritization instincts.
Ability to communicate with C-level and deeply technical stakeholders.
Comfort running technical workshops.
Ability to translate ambiguity into architecture.

Nice To Haves

Availability to work from or relocate to a preferred location: Dallas, TX; Boston, MA; New York, NY; Seattle, WA; or Chicago, IL.

Responsibilities

Design and implement integrations between client systems and CloudFactory’s platform.
Architect scalable agentic AI and human-in-the-loop workflows.
Define data ingestion, transformation, and feedback loops.
Evaluate system bottlenecks, including latency, quality, and throughput.
Translate proof-of-concept AI systems into scalable production workflows.
Identify operational risks before deployment.
Partner with Delivery teams to ensure execution feasibility.
Improve reliability and reduce manual intervention.
Build APIs, connectors, automation scripts, and data pipelines.
Debug integration issues in client environments.
Contribute to internal platform enhancements.
Surface recurring patterns from client deployments.
Distinguish between custom solutions and reusable features.
Influence roadmap priorities with evidence from the field.
Lead technical discovery sessions.
Support pre-sales validation.
Act as a trusted advisor to enterprise engineering teams.