Job Description
As a “Forward Deployment Engineer” at Relyance AI, you’ll operate like a technical owner for your customers: run deep discovery, build and deploy real solutions, and drive adoption through hands-on execution. You’ll work directly in customer environments (cloud + data ecosystems), troubleshoot gnarly issues, and create the assets—integrations, automation, runbooks, templates—that accelerate time-to-value. Because we’re building in a fast-evolving space (privacy, data security, and AI governance), you’ll also help define “what good looks like” by partnering tightly with Product and Engineering and shaping product direction based on what you see in the field.
What You’ll Do:
Run deep, structured discovery with customer stakeholders (Security/Privacy/Data/Engineering): listen hard, ask incisive follow-ups, uncover root causes, and translate pain into clear outcomes and an actionable technical plan leveraging the Relyance AI platform.
Own the technical solution end-to-end—from architecture and integration design through implementation—making pragmatic tradeoffs that balance speed, security, and long-term maintainability.
Implement and ship: configure the platform, stand up integrations, validate outcomes, troubleshoot in real time, and deliver working deployments (not just recommendations).
Build technical assets that accelerate success: repeatable rollout playbooks, runbooks, reference architectures, automation/scripts, validation checklists, and “starter” templates that customers can adopt and scale.
Drive measurable outcomes and adoption by defining success metrics, establishing baselines, and running enablement (workshops, working sessions, office hours) that turn features into sustained usage.
Operate as the technical owner for your accounts: maintain a living technical account plan, proactively identify expansion opportunities, and partner with Sales to convert technical wins into renewals and growth.
Lead escalations hands-on: reproduce issues, isolate root causes, coordinate with Engineering/Support, and deliver high-quality RCAs and prevention plans—then verify fixes and close the loop with customers.
Shape product direction with concrete customer evidence: synthesize discovery patterns, propose product gaps and solutions, and work closely with Product/Engineering—especially with early adopters—to refine product fit.
Communicate with clarity and precision: concise updates, strong stakeholder management, and crisp articulation of risks, timelines, and tradeoffs for both technical and non-technical audiences.
Must have skills:
Deep discovery leadership: Ability to run structured discovery, listen for the real problem, ask sharp follow-ups, map stakeholders, and translate customer pain into clear outcomes + a concrete technical plan.
Hands-on solution delivery: Proven ability to design, implement, and validate solutions — from integration planning through deployment and post-go-live optimization.
Customer-facing technical + account ownership: Experience owning technical relationships and outcomes in roles like TAM, Solutions Engineer, Implementation Lead, Support/Systems Engineer, etc.—comfortable being the technical owner for an account.
Cloud deployment experience: Hands-on experience designing and deploying solutions in AWS, GCP, or Azure (architecture, services, permissions, networking basics, operational considerations).
Strong data ecosystem understanding: Working knowledge of modern data landscapes—sources, pipelines, storage/warehouses/lakes, data movement, observability, and how tools connect across teams.
Integration and troubleshooting proficiency: Comfortable with APIs (REST), auth patterns (OAuth/SSO concepts), logs/debugging, and integration validation tools (e.g., Postman). Bonus if you can script/automate and reason about data (SQL).
AI-powered building mindset: Proficiency in leveraging AI tools to build—prototypes, automations, accelerators, internal utilities, and customer-facing workflows/demos that speed delivery.
High ownership + detail orientation: Strong accountability, follow-through, and operational rigor—able to run multiple workstreams, track risks, and deliver reliably.
Clear communication for mixed audiences: Ability to explain complex technical concepts to both technical and non-technical stakeholders; strong stakeholder management and crisp written updates.
Early-product / GTM maturity: Comfort working with new or emerging products—guiding early adopters, creating repeatable playbooks, and shaping product fit via structured feedback to Product/Engineering.
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