Parloa
Deployment Strategist - US
New York Office · Exec
Sponsorship not specifiedDetected 8 days ago
TypeScriptPythonAWSGCPAzureKubernetesTerraformMachine LearningLLMsAgentic AIComplianceProduct ManagementProduct StrategySAPCustomer SuccessCustomer SupportLeadershipAdaptability
About the role
- Land big clients (Global 2000 with complex, off-roadmap needs) and ensure their AI transformation succeeds.
- Translate customer needs into product capabilities (not one-off customizations) that become reusable platform features.
- Represent customers to Parloa Product and represent Parloa Product to customers, bridging both worlds.
Responsibilities
- Manage stakeholders across business and tech (CEO, COO, CIO, CTO, VP Support), owning deployment from value thesis through production to expansion.
- Build prototypes, make product changes, and create business plans hands-on - execution over PowerPoints.
- Influence without authority across Product, Engineering, Sales, and Customer Success to drive outcomes.
- When millions of people reach out to a brand, those interactions aren't just support tickets; they're defining experiences.
- Own hard conversations: tradeoffs, "no," re-scoping, and timeline realism with strategic accounts.
- As agentic AI accelerates, Parloans are shaping the foundation of a new era in customer experience, one where customer support is no longer transactions, but meaningful exchanges.
Requirements
- 7-12 years of experience in enterprise software (solution architecture, product, or consulting) with a proven track record of customer-facing delivery.
- "Field CPO" mentality: ability to translate customer outcomes into product reality and vice versa.
Nice to have
- Industry-changing companies: worked at companies that transformed their industry ( Booking.com, Decathlon, N26, Deel, Stripe, Uber, Airbnb).
- Hypergrowth experience: thrived in fast-scaling environments (e.g., 25 to 1500 people).
- Background as Palantir Deployment Strategist, AWS/GCP Solutions Architect, or product roles at hypergrowth tech.
- Tech stack: architectural understanding of Python, TypeScript, Terraform, K8s, Azure/AWS.
- AI/ML: LLM pipelines, agentic AI deployment, prompt engineering.
- Your Recruiting Process at Parloa:
- TA Screen → Hiring Manager Call → Case Study (take-home) → Case Study Presentation → Bar Raiser Interview
- We're at the beginning of a new era in customer experience, one where AI doesn't just respond, but understands, reasons, and takes action.
Skills
- architectural understanding of Python, TypeScript, Terraform, K8s, Azure/AWS.
- LLM pipelines, agentic AI deployment, prompt engineering.
Compensation
- $220,000 - $255,000 USD
Benefits
- We provide equal opportunities to all qualified applicants regardless race, gender, sexual orientation, age, religion, national origin, disability status, socioeconomic background and other characteristics.
Company info
- making every conversation seamless, intelligent, and genuinely helpful.
- If you care about shaping how businesses and customers connect at scale and want your work to matter in real, everyday moments, this is where you do it.
- At Parloa, ownership isn't a buzzword; it means being accountable for outcomes, not just tasks.
- We operate in a category that's evolving fast, where the bar is high, and the problems are complex.
- We hire people who think in solutions, communicate with clarity, and follow through.
- People who are comfortable making decisions, taking responsibility, and raising the standard for themselves and those around them.
- As our Deployment Strategist, you will drive AI transformation at Global 2000 enterprises as the "Field CPO" for Parloa's most strategic customers.
- You will own the bridge between customer outcomes and product reality, translating business needs into deployable product capabilities for customers like Booking.com, Allianz, Decathlon, and TUI.
- Parloa's mission is to make every customer conversation feel effortless for both customers and the companies serving them.
This listing is sourced directly from Parloa's careers page and normalized into a canonical job model.