AI Forward Deployed Engineer

About us:

Hiver is a modern, AI-driven customer service platform used by companies across healthcare, finance, logistics, education, and technology. We help teams deliver fast, human support across email, chat, phone, WhatsApp, and more — without the complexity of legacy helpdesks.

We’re a challenger brand in a category dominated by over-engineered tools. We build software that is simple, powerful, and genuinely helpful, and we operate internally with that same philosophy. If you want meaningful ownership, thoughtful teammates, and work that ships, Hiver is a great place to do it.

Opportunity:

As an AI Forward Deployed Engineer (AI FDE) , you'll be part of Hiver's AI Engineering team, working directly with strategic customers to ensure successful adoption of Hiver AI.

You'll embed virtually with customers to understand how their support operations work, design and implement production-ready AI solutions, and help customers move from pilot to successful production deployments. Along the way, you'll uncover product gaps, influence our roadmap, and ensure that learnings from the field continuously improve our platform.

This is an engineering-first role—not Sales, Solutions Consulting, or Customer Support. Your success isn't measured by demos or quotas. It's measured by whether customers successfully deploy, trust, and adopt Hiver AI.

**Why does this role exist?
**
The AI team (2 PMs, 8 engineers) currently absorbs a steady stream of customer-facing technical work: AI agent onboarding calls, knowledge base setup, AOP/workflow configuration, "why did the AI say this?" escalations, and pilot tuning sessions. Every hour spent there is an hour not spent on roadmap execution. The AI FDE owns this surface end-to-end—they are the technical extension of the AI team embedded in customer reality, converting deployments into successful, self-sustaining rollouts and feeding structured signal back to product.

The single success test for this role: adoption of AI features in the accounts the AI FDE owns. Concretely, deployments that reach production autonomy (move up the trust ladder), sustained deflection/resolution rates post-onboarding, and expansion of AI feature usage (agents, Copilot, and Topics) in AI FDE-touched accounts vs. the rest. Freed-up PM/engineer time is the byproduct and a guardrail metric: within 2 quarters, the team should be pulled into customer calls only on explicit AI FDE escalation, not by default.

Department AI Employment Type Full Time Location Remote - India Workplace type Fully remote Reporting To Anurag Maherchandani

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This role's hiring manager:

Anurag Maherchandani

View Anurag's Profile __

Key Responsibilities

**What you will do
**

Own AI Deployments End-to-End

• Lead technical onboarding for AI Agents, Copilot, and other AI capabilities
• Configure knowledge sources, workflows, automation rules, and AI behaviors
• Integrate customer systems and enable production-ready deployments
• Define rollout plans and success criteria alongside customer stakeholders
• Continuously improve deployment playbooks and implementation best practices

**Become the First Line of Technical Investigation

** When customers ask: _"Why did the AI respond this way?"
_ You will:

• Investigate AI behavior using logs, traces, evaluation tooling, and retrieval diagnostics
• Identify whether issues stem from:
  • Knowledge quality
  • Retrieval failures
  • Prompt or workflow configuration
  • Product limitations
• Resolve issues through configuration whenever possible
• Escalate to engineering only with well-diagnosed, reproducible problems

Build Custom Solutions

• Develop production-quality integrations, scripts, and automations
• Build connectors using customer APIs
• Perform data migrations and knowledge-base transformations
• Prototype customer-specific solutions where product capabilities don't yet exist
• Convert recurring workarounds into product improvement proposals

Improve AI Quality

• Run structured AI quality reviews using evaluation frameworks
• Measure and improve response quality, deflection, and resolution rates
• Tune AI behavior using knowledge improvements, workflows, and automation logic
• Replace anecdotal feedback with measurable quality metrics

**Shape the Product

** As someone closest to customer deployments, you'll help influence the product roadmap by:

• Identifying recurring deployment friction
• Tracking feature gaps and customer pain points
• Sharing structured field insights with Product and Engineering
• Helping distinguish between product improvements and implementation best practices

Enable Internal Teams

• Create deployment playbooks and troubleshooting guides
• Train Customer Success teams on AI administration and diagnostics
• Reduce dependency on Product and Engineering for routine customer issues
• Help scale AI deployments through documentation and operational excellence

**What We're Looking For

** Technical

• Full-stack engineering fundamentals — 3–6 years of software engineering experience, able to ship production-quality code independently (Python + TypeScript/JS is the likely fit for our stack). Not a scripter; a real engineer.
• APIs & integrations — comfortable reading a customer's API docs, building connectors, moving data between systems (email systems, helpdesks, CRMs, webhooks).
• LLM systems literacy — understands RAG pipelines, retrieval failure modes, prompt/instruction design, vector search, and why an AI answer went wrong. Hands-on with eval/observability tooling (we use Langfuse-style tracing, LLM-as-judge, golden datasets — they should be able to read and extend these).
• Data skills — strong SQL, comfort with messy customer data, log analysis at scale.
• Production debugging — can investigate live issues methodically: reproduce, isolate, root-cause, document.
• Cloud/infra basics — enough AWS/GCP to understand deployment constraints, auth (OAuth/SSO), and data security questions customers will ask.

Non-technical

• Customer-facing composure — can run a call with a frustrated support ops leader, set expectations honestly, and leave them more confident than before.
• Translation both ways — turns vague customer complaints into precise technical diagnoses, and technical constraints into plain business language.
• Ownership under ambiguity — thrives with incomplete requirements; scopes an MVP fix, ships it, iterates. Doesn't wait for a ticket to be perfectly specified.
• Judgment on escalation — knows the difference between "I can fix this with config" and "this is a product gap engineering must see," and doesn't cry wolf.
• Documentation discipline — playbooks, runbooks, and field reports are half the job. If it isn't written down, the time savings don't compound.
• Prioritization across accounts — will juggle multiple deployments; needs to manage their own queue without a PM directing traffic.
• Maintain work-hour overlap with Engineering, US-based teams, and customers to foster effective cross-functional collaboration.

Nice to Have

• Experience working in Customer Support, Helpdesk, or CX SaaS
• Previous experience as a Solutions Engineer, Implementation Engineer, Customer Engineer, or AI Forward Deployed Engineer
• Experience deploying AI solutions in production for enterprise customers
• Familiarity with security, compliance, and data privacy requirements in SaaS environments

What This Role Is Not

This role is not

• A Sales Engineer focused on demos or pre-sales
• A traditional Customer Support role
• A professional services consultant building one-off solutions

Every customer engagement should either:

• Improve the product,
• Become a reusable playbook, or
• Create a scalable implementation pattern.

How Success Is Measured

You'll be successful when the customers you own consistently adopt and expand their use of Hiver AI.

Key success metrics include:

North star — AI feature adoption in FDE-owned accounts:

• % of deployments reaching production autonomy (trust-ladder progression), and time-to-live (kickoff → production).
• Deflection/resolution rates sustained 60+ days post-onboarding (not just at go-live).
• AI feature expansion within AI FDE-touched accounts (agents → Copilot → Topics) vs. untouched accounts.

Guardrail metrics — the team gets its time back:

• Hours/month PMs and engineers spend on customer calls (baseline now; target 60–70% reduction in 2 quarters).
• Escalations reaching engineering that arrive pre-diagnosed and reproduced (target: >90%).
• % of deployment steps covered by playbooks/runbooks.

Who Should Apply

This role is best suited for engineers who enjoy combining software engineering with customer impact.

You will certainly thrive here if you enjoy:

• Building production systems
• Working directly with customers
• Solving real-world AI deployment challenges
• Owning problems end-to-end
• Influencing product direction through customer insights

This is an SDE-2 level role requiring strong technical judgement, customer-facing confidence, and the ability to independently drive successful AI deployments.

About Hiver

We specialise in delivering innovative solutions and exceptional services to meet the diverse needs of our clients. With a strong commitment to quality and customer satisfaction, we strive to exceed expectations and drive success in every project we undertake.

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