Truelogic Implementation Engineer at Truelogic owns technical implementation workstreams from scoping through go-live. The role involves building integrations between an AI-powered platform and various tools and systems.
Responsibilities
Own implementation workstreams end-to-end, from technical scoping and planning through configuration, testing, and go-live.
Build and configure integrations between the platform and tools used by agencies and customers, including Figma, Slack, Microsoft Teams, Adobe, and performance dashboards.
Evaluate integration approaches, data mappings, configuration requirements, and technical trade-offs, providing well-reasoned recommendations.
Collaborate closely with Product, Engineering, and agency stakeholders throughout implementation and rollout activities.
Identify and proactively communicate technical risks, blockers, dependencies, and trade-offs.
Understand customer and agency data environments, workflows, and pain points to help design tailored onboarding and implementation solutions.
Support the development of repeatable implementation playbooks, technical documentation, training materials, and onboarding processes.
Help onboard and informally mentor junior implementation engineers.
Leverage AI-enabled development tools and workflows for engineering, automation, debugging, and operational tasks.
Orchestrate multi-step AI and agent-driven workflows, selecting appropriate models, tools, and execution patterns for different use cases.
Review and harden AI-assisted outputs to ensure security, scalability, maintainability, reliability, and architectural fit.
Support prompts, evaluation frameworks, documentation, and operational context systems that improve engineering velocity and reliability.
Qualification
Working knowledge of APIsStrong communication skills
Required
2–5 years of experience in implementation engineering, solutions engineering, technical delivery, or a related technical role, ideally within SaaS, data, analytics, or technology environments.
Working knowledge of APIs, data systems, integrations, and cloud-based architectures such as Google Cloud, Azure, or similar platforms.
Demonstrated ability to independently deliver high-quality technical implementation work on complex projects.
Strong communication skills, with the ability to clearly present technical work, recommendations, and reasoning to both technical and non-technical audiences.
Experience collaborating with cross-functional teams across Product, Engineering, and other business or client-facing functions.
Comfortable working in ambiguous environments and adapting to rapidly evolving requirements and priorities.
Strong problem-solving skills and a growth mindset, with a demonstrated commitment to continuously improving technical skills.
Comfortable using AI-enabled development tools and workflows to accelerate engineering, automation, debugging, and operational tasks.
Experience orchestrating multi-step AI or agent-driven workflows, including selecting appropriate models, tools, and execution patterns based on the use case.
Strong judgment when reviewing and hardening AI-assisted outputs, with consideration for security, scalability, maintainability, reliability, and architectural fit.
Experience creating or maintaining prompts, evaluation frameworks, technical documentation, or operational context systems that improve engineering velocity and reliability.
Familiarity with automated evaluation and feedback loops for AI-enabled systems and workflows.
