AI Implementation Engineer

Who We Are

Ema is building the world’s leading Agentic AI platform to transform enterprise productivity. We enable organizations to delegate repetitive tasks to Ema, the Universal AI Employee, delivering 10x gains in workforce efficiency, across functions. Our team includes engineers from premier tech companies and graduates of Stanford, MIT, UC Berkeley, CMU, and IITs. We are backed by industry leading investors including Accel, Naspers/Prosus, Section32, and angels like Sheryl Sandberg and Dustin Moskovitz.

Who You Are

The AI Implementation Engineer owns the technical delivery and stabilization of Ema's agentic AI solutions in customer environments — from commitment through production rollout and steady state. This is a hands-on, post-sales, customer-facing engineering role: you build, you deliver, and you are the technical anchor the customer leans on. You thrive in ambiguity, make abstract problems concrete, and reduce chaos rather than amplify it when things go wrong.

What You'll Work On

• End-to-End AI Delivery Ownership — Own technical delivery from design alignment through production rollout and stabilization; configure, extend, and integrate Ema's agentic AI platform to meet customer requirements; ensure solutions align with Ema's agentic architecture and platform capabilities
• Hands-On Engineering — Write clean, efficient, maintainable code to build customer integrations, custom agents, and workflow extensions; build and maintain APIs (REST, gRPC) and integrations across enterprise SaaS systems; work with back-end languages such as Python and Go, and contribute to front-end interfaces (React/Angular, HTML, CSS, JavaScript) where customer-facing tooling is needed; work with data stores such as PostgreSQL, Clickhouse, Elastic, and Redis
• Feasibility Judgment & Agentic Workflow Translation — Develop deep understanding of each customer's business processes, systems, and constraints; translate business workflows into feasible agentic AI workflows — and push back when something shouldn't be built; anticipate where AI implementations break: integrations, data quality, scale, edge cases
• Customer Leadership (Post-Sales) — Be the primary technical point of contact for customer business and IT stakeholders during implementation; coach customer teams and internal partners during high-stress phases — go-lives, incidents, scope changes; communicate progress, risks, and decisions clearly across technical and executive audiences
• Production Readiness & Stabilization — Stand systems up in multi-tenant SaaS environments and harden them for production; apply security best practices and enterprise integration patterns (auth, RBAC, audit, compliance); track success through adoption signals and outcome metrics — not just feature shipment; stabilize systems post go-live under real pressure
• Cross-Functional Collaboration — Coordinate across Ema Engineering, Product, Data, Infrastructure, and Value Engineering; feed customer learnings back into product and platform improvements; contribute to shared standards, delivery discipline, and reusable patterns across the implementation team

Ideally, You'd Have

• 5–8 years of relevant experience in technical implementation, post-sales