AI Implementation Engineer
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Job Description
AI Implementation Engineer managing the technical delivery and stabilization of agentic AI solutions for customers. Collaborating with various teams to ensure successful implementation and production rollout.
Responsibilities:
- 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
- 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 to shape scalable, extensible schemas for customer deployments
- 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
- 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
- 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
- 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
Requirements:
- 5–8 years of relevant experience in technical implementation, post-sales engineering, solutions engineering, or hands-on software engineering with significant customer-facing exposure
- Bachelor's degree in Computer Science or related field
- Hands-on production experience with agentic AI, automation, LLM applications, or workflow orchestration platforms — beyond pilots
- Strong back-end engineering skills in Python and/or Go; solid foundations in algorithms, data structures, and object-oriented programming
- Experience designing and building APIs (REST, gRPC) and integrations across enterprise systems
- Working knowledge of databases (PostgreSQL, Elastic, Redis, Clickhouse) and front-end frameworks (React or Angular)
- Experience with cloud platforms (AWS, GCP, Azure) and containerization (Docker, Kubernetes)
- Experience deploying and operating software in multi-tenant SaaS environments
- Understanding of security best practices and protocols for enterprise software
- Track record of owning customer-facing delivery end-to-end — production, scale, and accountability
- Background in fast-growing startups or enterprise platform companies
- Strong technical judgment, calm under pressure, and excellent written and verbal communication with both engineers and business stakeholders
- Experience working with global, distributed teams.
Benefits:
- Health insurance
- Retirement plans
- Paid time off
- Flexible work arrangements
- Professional development
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