AI Implementation Engineer

Requirements

• You are equally comfortable writing production code, debugging an integration the night before a go-live, walking a customer’s VP of Operations through an architecture decision and translating a messy business problem into a feasible agentic workflow

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• You thrive in ambiguity, make abstract problems concrete, and reduce chaos rather than amplify it when things go wrong

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• 5–8 years of relevant experience in technical implementation, post-sales engineering, solutions engineering, or hands-on software engineering with significant customer-facing exposure

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• Bachelor’s degree in Computer Science or related field

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• Hands-on production experience with agentic AI, automation, LLM applications, or workflow orchestration platforms — beyond pilots

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• Strong back-end engineering skills in Python and/or Go; solid foundations in algorithms, data structures, and object-oriented programming

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• Experience designing and building APIs (REST, gRPC) and integrations across enterprise systems

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• Working knowledge of databases (PostgreSQL, Elastic, Redis, Clickhouse) and front- end frameworks (React or Angular)

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• Experience with cloud platforms (AWS, GCP, Azure) and containerization (Docker, Kubernetes)

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• Experience deploying and operating software in multi-tenant SaaS environments

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• Understanding of security best practices and protocols for enterprise software

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• Track record of owning customer-facing delivery end-to-end — production, scale, and accountability

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• Background in fast-growing startups or enterprise platform companies

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• Strong technical judgment, calm under pressure, and excellent written and verbal communication with both engineers and business stakeholders

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• Experience working with global, distributed teams

What the job involves

• 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

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• 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

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• You’ll work closely with Value Engineering, Product, Engineering, Infrastructure, and the customer’s IT and business teams to prove that agentic AI can be implemented responsibly — not heroically

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• Own technical delivery from design alignment through production rollout and Stabilization

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• Configure, extend, and integrate Ema’s agentic AI platform to meet customer requirements

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• Ensure solutions align with Ema’s agentic architecture and platform capabilities

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• Write clean, efficient, maintainable code to build customer integrations, custom agents, and workflow extensions

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• Build and maintain APIs (REST, gRPC) and integrations across enterprise SaaS systems

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• 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

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• Work with data stores such as PostgreSQL, Clickhouse, Elastic, and Redis to shape scalable, extensible schemas for customer deployments

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• Develop deep understanding of each customer’s business processes, systems, and constraints

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• Translate business workflows into feasible agentic AI workflows — and push back when something shouldn’t be built

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• Anticipate where AI implementations break: integrations, data quality, scale, edge cases

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• Be the primary technical point of contact for customer business and IT stakeholders during implementation

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• Coach customer teams and internal partners during high-stress phases — go-lives, incidents, scope changes

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• Communicate progress, risks, and decisions clearly across technical and executive audiences

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• Stand systems up in multi-tenant SaaS environments and harden them for production

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• Apply security best practices and enterprise integration patterns (auth, RBAC, audit, compliance)

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• Track success through adoption signals and outcome metrics — not just feature shipment

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• Stabilize systems post go-live under real pressure

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• Coordinate across Ema Engineering, Product, Data, Infrastructure, and Value Engineering

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• Feed customer learnings back into product and platform improvements

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• Contribute to shared standards, delivery discipline, and reusable patterns across the implementation team

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